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"SGLD": {"title": "Bayesian Learning via Stochastic Gradient Langevin Dynamics", "year": 2011}, "Sparse GP (SVGP)": {"title": "Gaussian Processes for Big Data", "year": 2013, "url": "https://arxiv.org/abs/1309.6835"}, "Deep Kernel Learning": {"title": "Deep Kernel Learning", "year": 2016, "url": "https://arxiv.org/abs/1511.02222"}, "AlphaFold 2": {"title": "Highly accurate protein structure prediction with AlphaFold", "year": 2021, "url": "https://doi.org/10.1038/s41586-021-03819-2"}, "ESMFold": {"title": "Evolutionary-scale prediction of atomic-level protein structure with a language model", "year": 2023, "url": "https://doi.org/10.1101/2022.07.20.500902"}, "DiffDock": {"title": "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking", "year": 2023, "url": "https://arxiv.org/abs/2210.01776"}, "SchNet": {"title": "SchNet: A continuous-filter convolutional neural network for modeling quantum interactions", "year": 2017, "url": "https://arxiv.org/abs/1706.08566"}, "GemNet": 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Consistent, Dynamic, and Extendable Long Video Generation from Text", "year": 2025, "url": "https://arxiv.org/abs/2403.14773"}, "PredRNN": {"title": "PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs", "year": 2017}, "SimVP": {"title": "SimVP: Simpler yet Better Video Prediction", "year": 2022, "url": "https://arxiv.org/abs/2206.05099"}, "Tune-A-Video": {"title": "Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation", "year": 2023, "url": "https://arxiv.org/abs/2212.11565"}, "TokenFlow": {"title": "TokenFlow: Consistent Diffusion Features for Consistent Video Editing", "year": 2024, "url": "https://arxiv.org/abs/2307.10373"}, "SadTalker": {"title": "SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation", "year": 2023, "url": "https://arxiv.org/abs/2211.12194"}, "Animate Anyone": {"title": "Animate Anyone: Consistent and Controllable Image-to-Video Synthesis 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Models", "year": 2023, "url": "https://arxiv.org/abs/2301.12503"}, "Wav2Lip": {"title": "A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild", "year": 2020, "url": "https://arxiv.org/abs/2008.10010"}, "DreamFusion (SDS)": {"title": "DreamFusion: Text-to-3D using 2D Diffusion", "year": 2023, "url": "https://arxiv.org/abs/2209.14988"}, "Magic3D": {"title": "Magic3D: High-Resolution Text-to-3D Content Creation", "year": 2023, "url": "https://arxiv.org/abs/2211.10440"}, "MVDream": {"title": "MVDream: Multi-view Diffusion for 3D Generation", "year": 2024, "url": "https://arxiv.org/abs/2308.16512"}, "Point-E": {"title": "Point-E: A System for Generating 3D Point Clouds from Complex Prompts", "year": 2022, "url": "https://arxiv.org/abs/2212.08751"}, "GET3D": {"title": "GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images", "year": 2022, "url": "https://arxiv.org/abs/2209.11163"}, "ATISS": {"title": "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", "year": 2021, "url": "https://arxiv.org/abs/2110.03675"}, "LayoutGPT": {"title": "LayoutGPT: Compositional Visual Planning and Generation with Large Language Models", "year": 2023, "url": "https://arxiv.org/abs/2305.15393"}, "GIRAFFE": {"title": "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields", "year": 2021, "url": "https://arxiv.org/abs/2011.12100"}, "EG3D": {"title": "Efficient Geometry-aware 3D Generative Adversarial Networks", "year": 2022, "url": "https://arxiv.org/abs/2112.07945"}, "AvatarCLIP": {"title": "AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars", "year": 2022, "url": "https://arxiv.org/abs/2205.08535"}, "DreamAvatar": {"title": "DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models", "year": 2024, "url": "https://arxiv.org/abs/2304.00916"}, "MAV3D": {"title": "Text-To-4D Dynamic Scene Generation", "year": 2023, "url": "https://arxiv.org/abs/2301.11280"}, "4DGen": {"title": "4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency", "year": 2023, "url": "https://arxiv.org/abs/2312.17225"}, "JT-VAE": {"title": "Junction Tree Variational Autoencoder for Molecular Graph Generation", "year": 2018, "url": "https://arxiv.org/abs/1802.04364"}, "EDM (molecules)": {"title": "Equivariant Diffusion for Molecule Generation in 3D", "year": 2022, "url": "https://arxiv.org/abs/2203.17003"}, "ProteinMPNN": {"title": "Robust deep learning-based protein sequence design using ProteinMPNN", "year": 2022, "url": "https://doi.org/10.1101/2022.06.03.494563"}, "RFdiffusion": {"title": "De novo design of protein structure and function with RFdiffusion", "year": 2023, "url": "https://doi.org/10.1038/s41586-023-06415-8"}, "CDVAE": {"title": "Crystal Diffusion Variational Autoencoder for Periodic Material Generation", "year": 2022, "url": "https://arxiv.org/abs/2110.06197"}, "MatterGen": {"title": "A generative model for inorganic materials design", "year": 2025, "url": "https://arxiv.org/abs/2312.03687"}, "Codex": {"title": "Evaluating Large Language Models Trained on Code", "year": 2021, "url": "https://arxiv.org/abs/2107.03374"}, "AlphaCode": {"title": "Competition-Level Code Generation with AlphaCode", "year": 2022, "url": "https://arxiv.org/abs/2203.07814"}, "StarCoder": {"title": "StarCoder: may the source be with you!", "year": 2023, "url": "https://arxiv.org/abs/2305.06161"}, "DatasetGAN": {"title": "DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort", "year": 2021, "url": "https://arxiv.org/abs/2104.06490"}, "DA-Fusion": {"title": "Effective Data Augmentation With Diffusion Models", "year": 2024, "url": "https://arxiv.org/abs/2302.07944"}, "DDPM": {"title": "Denoising Diffusion Probabilistic Models", "year": 2020, "url": "https://arxiv.org/abs/2006.11239"}, "Score SDE": {"title": "Score-Based Generative Modeling through Stochastic Differential Equations", "year": 2021, "url": "https://arxiv.org/abs/2011.13456"}, "EDM": {"title": "Elucidating the Design Space of Diffusion-Based Generative Models", "year": 2022, "url": "https://arxiv.org/abs/2206.00364"}, "Flow Matching": {"title": "Flow Matching for Generative Modeling", "year": 2023, "url": "https://arxiv.org/abs/2210.02747"}, "Rectified Flow": {"title": "Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow", "year": 2023, "url": "https://arxiv.org/abs/2209.03003"}, "VQ-VAE-2": {"title": "Generating Diverse High-Fidelity Images with VQ-VAE-2", "year": 2019, "url": "https://arxiv.org/abs/1906.00446"}, "MaskGIT": {"title": "MaskGIT: Masked Generative Image Transformer", "year": 2022, "url": "https://arxiv.org/abs/2202.04200"}, "VAR": {"title": "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction", "year": 2024, "url": "https://arxiv.org/abs/2404.02905"}, "StyleGAN": {"title": "A Style-Based Generator Architecture for Generative Adversarial Networks", "year": 2019, "url": "https://arxiv.org/abs/1812.04948"}, "BigGAN": {"title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis", "year": 2019, "url": "https://arxiv.org/abs/1809.11096"}, "VAE": {"title": "Auto-Encoding Variational Bayes", "year": 2014, "url": "https://arxiv.org/abs/1312.6114"}, "Glow": {"title": "Glow: Generative Flow with Invertible 1x1 Convolutions", "year": 2018, "url": "https://arxiv.org/abs/1807.03039"}, "IGEBM": {"title": "Implicit Generation and Modeling with Energy Based Models", "year": 2019, "url": "https://arxiv.org/abs/1903.08689"}, "JEM": {"title": "Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One", "year": 2020, "url": "https://arxiv.org/abs/1912.03263"}, "DDIM": {"title": "Denoising Diffusion Implicit Models", "year": 2021, "url": "https://arxiv.org/abs/2010.02502"}, "DPM-Solver": {"title": "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps", "year": 2022, "url": "https://arxiv.org/abs/2206.00927"}, "Consistency Models": {"title": "Consistency Models", "year": 2023, "url": "https://arxiv.org/abs/2303.01469"}, "Classifier Guidance": {"title": "Diffusion Models Beat GANs on Image Synthesis", "year": 2021, "url": "https://arxiv.org/abs/2105.05233"}, "Classifier-Free Guidance": {"title": "Classifier-Free Diffusion Guidance", "year": 2022, "url": "https://arxiv.org/abs/2207.12598"}, "GFlowNets": {"title": "Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation", "year": 2021, "url": "https://arxiv.org/abs/2106.04399"}, "Diffusion samplers": {"title": "Path Integral Sampler: a stochastic control approach for sampling", "year": 2022, "url": "https://arxiv.org/abs/2111.15141"}, "SimCLR": {"title": "A Simple Framework for Contrastive Learning of Visual Representations", "year": 2020, "url": "https://arxiv.org/abs/2002.05709"}, "MoCo": {"title": "Momentum Contrast for Unsupervised Visual Representation Learning", "year": 2020, "url": "https://arxiv.org/abs/1911.05722"}, "BYOL": {"title": "Bootstrap your own latent: A new approach to self-supervised Learning", "year": 2020, "url": "https://arxiv.org/abs/2006.07733"}, "BERT": {"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", "year": 2019, "url": "https://arxiv.org/abs/1810.04805"}, "MAE": {"title": "Masked Autoencoders Are Scalable Vision Learners", "year": 2022, "url": "https://arxiv.org/abs/2111.06377"}, "BEiT": {"title": "BEiT: BERT Pre-Training of Image Transformers", "year": 2022, "url": "https://arxiv.org/abs/2106.08254"}, "DINO": {"title": "Emerging Properties in Self-Supervised Vision Transformers", "year": 2021, "url": "https://arxiv.org/abs/2104.14294"}, "iBOT": {"title": "iBOT: Image BERT Pre-Training with Online Tokenizer", "year": 2022, "url": "https://arxiv.org/abs/2111.07832"}, "DINOv2": {"title": "DINOv2: Learning Robust Visual Features without Supervision", "year": 2023, "url": "https://arxiv.org/abs/2304.07193"}, "I-JEPA": {"title": "Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture", "year": 2023, "url": "https://arxiv.org/abs/2301.08243"}, "V-JEPA": {"title": "Revisiting Feature Prediction for Learning Visual Representations from Video", "year": 2024, "url": "https://arxiv.org/abs/2404.08471"}, "β-VAE": {"title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework", "year": 2017}, "FactorVAE": {"title": "Disentangling by Factorising", "year": 2018, "url": "https://arxiv.org/abs/1802.05983"}, "Slot Attention": {"title": "Object-Centric Learning with Slot Attention", "year": 2020, "url": "https://arxiv.org/abs/2006.15055"}, "MONet": {"title": "MONet: Unsupervised Scene Decomposition and Representation", "year": 2019, "url": "https://arxiv.org/abs/1901.11390"}, "node2vec": {"title": "node2vec: Scalable Feature Learning for Networks", "year": 2016, "url": "https://arxiv.org/abs/1607.00653"}, "GraphCL": {"title": "Graph Contrastive Learning with Augmentations", "year": 2020, "url": "https://arxiv.org/abs/2010.13902"}, "GPT-3": {"title": "Language Models are Few-Shot Learners", "year": 2020, "url": "https://arxiv.org/abs/2005.14165"}, "LLaMA": {"title": "LLaMA: Open and Efficient Foundation Language Models", "year": 2023, "url": "https://arxiv.org/abs/2302.13971"}, "BEiT-3": {"title": "Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks", "year": 2023, "url": "https://arxiv.org/abs/2208.10442"}, "EVA": {"title": "EVA: Exploring the Limits of Masked Visual Representation Learning at Scale", "year": 2023, "url": "https://arxiv.org/abs/2211.07636"}, "SatMAE": {"title": "SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery", "year": 2022, "url": "https://arxiv.org/abs/2207.08051"}, "Prithvi": {"title": "Foundation Models for Generalist Geospatial Artificial Intelligence", "year": 2023, "url": "https://arxiv.org/abs/2310.18660"}, "ESM-2": {"title": "Evolutionary-scale prediction of atomic-level protein structure with a language model", "year": 2023, "url": "https://doi.org/10.1101/2022.07.20.500902"}, "DNABERT": {"title": "DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome", "year": 2021, "url": "https://doi.org/10.1101/2020.09.17.301879"}, "DAPT / TAPT": {"title": "Don't Stop Pretraining: Adapt Language Models to Domains and Tasks", "year": 2020, "url": "https://arxiv.org/abs/2004.10964"}, "RoPE": {"title": "RoFormer: Enhanced Transformer with Rotary Position Embedding", "year": 2021, "url": "https://arxiv.org/abs/2104.09864"}, "ALiBi": {"title": "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation", "year": 2022, "url": "https://arxiv.org/abs/2108.12409"}, "YaRN": {"title": "YaRN: Efficient Context Window Extension of Large Language Models", "year": 2024, "url": "https://arxiv.org/abs/2309.00071"}, "Triplet Loss": {"title": "Deep Metric Learning Using Triplet Network", "year": 2015, "url": "https://arxiv.org/abs/1412.6622"}, "Sentence-BERT": {"title": "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", "year": 2019, "url": "https://arxiv.org/abs/1908.10084"}, "HashNet": {"title": "HashNet: Deep Learning to Hash by Continuation", "year": 2017, "url": "https://arxiv.org/abs/1702.00758"}, "Poincaré Embeddings": {"title": "Poincaré Embeddings for Learning Hierarchical Representations", "year": 2017, "url": "https://arxiv.org/abs/1705.08039"}, "CLIP": {"title": "Learning Transferable Visual Models From Natural Language Supervision", "year": 2021, "url": "https://arxiv.org/abs/2103.00020"}, "ALIGN": {"title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision", "year": 2021, "url": "https://arxiv.org/abs/2102.05918"}, "SigLIP": {"title": "Sigmoid Loss for Language Image Pre-Training", "year": 2023, "url": "https://arxiv.org/abs/2303.15343"}, "ImageBind": {"title": "ImageBind: One Embedding Space To Bind Them All", "year": 2023, "url": "https://arxiv.org/abs/2305.05665"}, "AV-HuBERT": {"title": "Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction", "year": 2022, "url": "https://arxiv.org/abs/2201.02184"}, "EGNN": {"title": "E(n) Equivariant Graph Neural Networks", "year": 2021, "url": "https://arxiv.org/abs/2102.09844"}, "SE(3)-Transformer": {"title": "SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks", "year": 2020, "url": "https://arxiv.org/abs/2006.10503"}, "PointNet++": {"title": "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space", "year": 2017, "url": "https://arxiv.org/abs/1706.02413"}, "DGCNN": {"title": "Dynamic Graph CNN for Learning on Point Clouds", "year": 2019, "url": "https://arxiv.org/abs/1801.07829"}, "Point Transformer": {"title": "Point Transformer", "year": 2021, "url": "https://arxiv.org/abs/2012.09164"}, "VQ-GAN": {"title": "Taming Transformers for High-Resolution Image Synthesis", "year": 2021, "url": "https://arxiv.org/abs/2012.09841"}, "MAGVIT": {"title": "MAGVIT: Masked Generative Video Transformer", "year": 2023, "url": "https://arxiv.org/abs/2212.05199"}, "SoundStream": {"title": "SoundStream: An End-to-End Neural Audio Codec", "year": 2022, "url": "https://arxiv.org/abs/2107.03312"}, "Hyperprior (Ballé)": {"title": "Variational image compression with a scale hyperprior", "year": 2018, "url": "https://arxiv.org/abs/1802.01436"}, "Linear Probes": {"title": "Understanding intermediate layers using linear classifier probes", "year": 2016, "url": "https://arxiv.org/abs/1610.01644"}, "CKA": {"title": "Similarity of Neural Network Representations Revisited", "year": 2019, "url": "https://arxiv.org/abs/1905.00414"}, "RepE": {"title": "Representation Engineering: A Top-Down Approach to AI Transparency", "year": 2023, "url": "https://arxiv.org/abs/2310.01405"}, "ReFT": {"title": "ReFT: Representation Finetuning for Language Models", "year": 2024, "url": "https://arxiv.org/abs/2404.03592"}, "Faster R-CNN": {"title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "year": 2015, "url": "https://arxiv.org/abs/1506.01497"}, "YOLO": {"title": "You Only Look Once: Unified, Real-Time Object Detection", "year": 2016, "url": "https://arxiv.org/abs/1506.02640"}, "DETR": {"title": "End-to-End Object Detection with Transformers", "year": 2020, "url": "https://arxiv.org/abs/2005.12872"}, "PointPillars": {"title": "PointPillars: Fast Encoders for Object Detection from Point Clouds", "year": 2019, "url": "https://arxiv.org/abs/1812.05784"}, "BEVFormer": {"title": "BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers", "year": 2022, "url": "https://arxiv.org/abs/2203.17270"}, "GLIP": {"title": "Grounded Language-Image Pre-training", "year": 2022, "url": "https://arxiv.org/abs/2112.03857"}, "OWL-ViT": {"title": "Simple Open-Vocabulary Object Detection with Vision Transformers", "year": 2022, "url": "https://arxiv.org/abs/2205.06230"}, "Grounding DINO": {"title": "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection", "year": 2024, "url": "https://arxiv.org/abs/2303.05499"}, "OpenPose": {"title": "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields", "year": 2017, "url": "https://arxiv.org/abs/1611.08050"}, "HRNet": {"title": "Deep High-Resolution Representation Learning for Human Pose Estimation", "year": 2019, "url": "https://arxiv.org/abs/1902.09212"}, "ViTPose": {"title": "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation", "year": 2022, "url": "https://arxiv.org/abs/2204.12484"}, "MDETR": {"title": "MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding", "year": 2021, "url": "https://arxiv.org/abs/2104.12763"}, "Kosmos-2": {"title": "Kosmos-2: Grounding Multimodal Large Language Models to the World", "year": 2023, "url": "https://arxiv.org/abs/2306.14824"}, "BMN": {"title": "BMN: Boundary-Matching Network for Temporal Action Proposal Generation", "year": 2019, "url": "https://arxiv.org/abs/1907.09702"}, "ActionFormer": {"title": "ActionFormer: Localizing Moments of Actions with Transformers", "year": 2022, "url": "https://arxiv.org/abs/2202.07925"}, "Moment-DETR": {"title": "QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries", "year": 2021, "url": "https://arxiv.org/abs/2107.09609"}, "UniVTG": {"title": "UniVTG: Towards Unified Video-Language Temporal Grounding", "year": 2023, "url": "https://arxiv.org/abs/2307.16715"}, "I3D": {"title": "Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset", "year": 2017, "url": "https://arxiv.org/abs/1705.07750"}, "SlowFast": {"title": "SlowFast Networks for Video Recognition", "year": 2019, "url": "https://arxiv.org/abs/1812.03982"}, "VideoMAE": {"title": "VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training", "year": 2022, "url": "https://arxiv.org/abs/2203.12602"}, "FaceNet": {"title": "FaceNet: A Unified Embedding for Face Recognition and Clustering", "year": 2015, "url": "https://arxiv.org/abs/1503.03832"}, "ArcFace": {"title": "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", "year": 2019, "url": "https://arxiv.org/abs/1801.07698"}, "Bilinear CNN": {"title": "Bilinear CNN Models for Fine-grained Visual Recognition", "year": 2015, "url": "https://doi.org/10.1109/iccv.2015.170"}, "TransFG": {"title": "TransFG: A Transformer Architecture for Fine-grained Recognition", "year": 2022, "url": "https://arxiv.org/abs/2103.07976"}, "wav2vec 2.0": {"title": "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations", "year": 2020, "url": "https://arxiv.org/abs/2006.11477"}, "Whisper": {"title": "Robust Speech Recognition via Large-Scale Weak Supervision", "year": 2023, "url": "https://arxiv.org/abs/2212.04356"}, "PANNs": {"title": "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition", "year": 2020, "url": "https://arxiv.org/abs/1912.10211"}, "AST": {"title": "AST: Audio Spectrogram Transformer", "year": 2021, "url": "https://arxiv.org/abs/2104.01778"}, "LayoutLM": {"title": "LayoutLM: Pre-training of Text and Layout for Document Image Understanding", "year": 2020, "url": "https://arxiv.org/abs/1912.13318"}, "Donut": {"title": "OCR-free Document Understanding Transformer", "year": 2022, "url": "https://arxiv.org/abs/2111.15664"}, "SORT": {"title": "Simple Online and Realtime Tracking", "year": 2016, "url": "https://arxiv.org/abs/1602.00763"}, "ByteTrack": {"title": 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"RankStats": {"title": "Automatically Discovering and Learning New Visual Categories with Ranking Statistics", "year": 2020, "url": "https://arxiv.org/abs/2002.05714"}, "GCD": {"title": "Generalized Category Discovery", "year": 2022, "url": "https://arxiv.org/abs/2201.02609"}, "FCN": {"title": "Fully Convolutional Networks for Semantic Segmentation", "year": 2015, "url": "https://arxiv.org/abs/1411.4038"}, "DeepLab": {"title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs", "year": 2018, "url": "https://arxiv.org/abs/1606.00915"}, "SegFormer": {"title": "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers", "year": 2021, "url": "https://arxiv.org/abs/2105.15203"}, "Mask R-CNN": {"title": "Mask R-CNN", "year": 2017, "url": "https://arxiv.org/abs/1703.06870"}, "Mask2Former": {"title": "Masked-attention Mask Transformer for Universal Image Segmentation", "year": 2022, "url": 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Radiance Field Rendering", "year": 2023, "url": "https://arxiv.org/abs/2308.04079"}, "2DGS": {"title": "2D Gaussian Splatting for Geometrically Accurate Radiance Fields", "year": 2024, "url": "https://arxiv.org/abs/2403.17888"}, "DeepSDF": {"title": "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation", "year": 2019, "url": "https://arxiv.org/abs/1901.05103"}, "Occupancy Networks": {"title": "Occupancy Networks: Learning 3D Reconstruction in Function Space", "year": 2019, "url": "https://arxiv.org/abs/1812.03828"}, "NeuS": {"title": "NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction", "year": 2021, "url": "https://arxiv.org/abs/2106.10689"}, "PCN": {"title": "PCN: Point Completion Network", "year": 2018, "url": "https://arxiv.org/abs/1808.00671"}, "PoinTr": {"title": "PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers", "year": 2021, "url": "https://arxiv.org/abs/2108.08839"}, "D-NeRF": {"title": "D-NeRF: Neural Radiance Fields for Dynamic Scenes", "year": 2021, "url": "https://arxiv.org/abs/2011.13961"}, "4D Gaussian Splatting": {"title": "4D Gaussian Splatting for Real-Time Dynamic Scene Rendering", "year": 2024, "url": "https://arxiv.org/abs/2310.08528"}, "HMR": {"title": "End-to-end Recovery of Human Shape and Pose", "year": 2018, "url": "https://arxiv.org/abs/1712.06584"}, "SMPLify": {"title": "Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image", "year": 2016, "url": "https://arxiv.org/abs/1607.08128"}, "PIFu": {"title": "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization", "year": 2019, "url": "https://arxiv.org/abs/1905.05172"}, "HumanNeRF": {"title": "HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video", "year": 2022, "url": "https://arxiv.org/abs/2201.04127"}, "HOnnotate": {"title": "HOnnotate: A method for 3D Annotation of Hand and Object Poses", "year": 2020, "url": 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Unknown Illumination", "year": 2021, "url": "https://arxiv.org/abs/2106.01970"}, "Diffusion Posterior Sampling": {"title": "Diffusion Posterior Sampling for General Noisy Inverse Problems", "year": 2023, "url": "https://arxiv.org/abs/2209.14687"}, "SVRG": {"title": "Accelerating Stochastic Gradient Descent using Predictive Variance Reduction", "year": 2013}, "Sharpness-Aware Minimization": {"title": "Sharpness-Aware Minimization for Efficiently Improving Generalization", "year": 2021, "url": "https://arxiv.org/abs/2010.01412"}, "CMA-ES": {"title": "The CMA Evolution Strategy: A Tutorial", "year": 2016, "url": "https://arxiv.org/abs/1604.00772"}, "Evolution Strategies": {"title": "Evolution Strategies as a Scalable Alternative to Reinforcement Learning", "year": 2017, "url": "https://arxiv.org/abs/1703.03864"}, "MGDA": {"title": "Multi-Task Learning as Multi-Objective Optimization", "year": 2018, "url": "https://arxiv.org/abs/1810.04650"}, "Extragradient": {"title": "A Variational 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"https://arxiv.org/abs/2103.02438"}, "DARTS": {"title": "DARTS: Differentiable Architecture Search", "year": 2019, "url": "https://arxiv.org/abs/1806.09055"}, "ENAS": {"title": "Efficient Neural Architecture Search via Parameter Sharing", "year": 2018, "url": "https://arxiv.org/abs/1802.03268"}, "Hyperband": {"title": "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization", "year": 2018, "url": "https://arxiv.org/abs/1603.06560"}, "Population-Based Training": {"title": "Population Based Training of Neural Networks", "year": 2017, "url": "https://arxiv.org/abs/1711.09846"}, "AlphaGo": {"title": "Mastering the game of Go with deep neural networks and tree search", "year": 2016, "url": "https://doi.org/10.1038/nature16961"}, "AlphaZero": {"title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play", "year": 2018, "url": "https://doi.org/10.1126/science.aar6404"}, "FunSearch": {"title": "Mathematical discoveries from program 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Your Language Model is Secretly a Reward Model", "year": 2023, "url": "https://arxiv.org/abs/2305.18290"}, "KTO": {"title": "KTO: Model Alignment as Prospect Theoretic Optimization", "year": 2024, "url": "https://arxiv.org/abs/2402.01306"}, "SimPO": {"title": "SimPO: Simple Preference Optimization with a Reference-Free Reward", "year": 2024, "url": "https://arxiv.org/abs/2405.14734"}, "GRPO": {"title": "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models", "year": 2024, "url": "https://arxiv.org/abs/2402.03300"}, "DeepSeek-R1": {"title": "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "year": 2025, "url": "https://arxiv.org/abs/2501.12948"}, "DQN": {"title": "Human-level control through deep reinforcement learning", "year": 2015, "url": "https://doi.org/10.1038/nature14236"}, "PPO": {"title": "Proximal Policy Optimization Algorithms", "year": 2017, "url": "https://arxiv.org/abs/1707.06347"}, "SAC": {"title": "Soft 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{"title": "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion", "year": 2023, "url": "https://arxiv.org/abs/2303.04137"}, "MaxEnt IRL": {"title": "Maximum Entropy Inverse Reinforcement Learning", "year": 2008}, "GAIL": {"title": "Generative Adversarial Imitation Learning", "year": 2016, "url": "https://arxiv.org/abs/1606.03476"}, "TransFuser": {"title": "Multi-Modal Fusion Transformer for End-to-End Autonomous Driving", "year": 2021, "url": "https://arxiv.org/abs/2104.09224"}, "UniAD": {"title": "Planning-oriented Autonomous Driving", "year": 2023, "url": "https://arxiv.org/abs/2212.10156"}, "ChauffeurNet": {"title": "ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst", "year": 2019, "url": "https://arxiv.org/abs/1812.03079"}, "PlanT": {"title": "PlanT: Explainable Planning Transformers via Object-Level Representations", "year": 2022, "url": "https://arxiv.org/abs/2210.14222"}, "ReAct": {"title": "ReAct: Synergizing Reasoning and Acting in 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"CyCADA": {"title": "CyCADA: Cycle-Consistent Adversarial Domain Adaptation", "year": 2018, "url": "https://arxiv.org/abs/1711.03213"}, "MixStyle": {"title": "Domain Generalization with MixStyle", "year": 2021, "url": "https://arxiv.org/abs/2104.02008"}, "SWAD": {"title": "SWAD: Domain Generalization by Seeking Flat Minima", "year": 2021, "url": "https://arxiv.org/abs/2102.08604"}, "DeViSE": {"title": "DeViSE: A Deep Visual-Semantic Embedding Model", "year": 2013}, "CLIP zero-shot": {"title": "Learning Transferable Visual Models From Natural Language Supervision", "year": 2021, "url": "https://arxiv.org/abs/2103.00020"}, "Prefix-Tuning": {"title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation", "year": 2021, "url": "https://arxiv.org/abs/2101.00190"}, "Prompt Tuning": {"title": "The Power of Scale for Parameter-Efficient Prompt Tuning", "year": 2021, "url": "https://arxiv.org/abs/2104.08691"}, "CoOp": {"title": "Learning to Prompt for Vision-Language Models", "year": 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"Equality of Opportunity in Supervised Learning", "year": 2016, "url": "https://arxiv.org/abs/1610.02413"}, "Fair Representations": {"title": "Learning Fair Representations", "year": 2013, "url": "https://proceedings.mlr.press/v28/zemel13.html"}, "StereoSet": {"title": "StereoSet: Measuring stereotypical bias in pretrained language models", "year": 2021, "url": "https://arxiv.org/abs/2004.09456"}, "Debiasing": {"title": "Man is to Computer Programmer as Woman is to Homemaker? 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"https://arxiv.org/abs/2309.06180"}, "Orca": {"title": "Orca: A Distributed Serving System for Transformer-Based Generative Models", "year": 2022}, "TVM": {"title": "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning", "year": 2018, "url": "https://arxiv.org/abs/1802.04799"}, "Triton": {"title": "Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations", "year": 2019, "url": "https://doi.org/10.1145/3315508.3329973"}, "Once-for-All": {"title": "Once-for-All: Train One Network and Specialize it for Efficient Deployment", "year": 2020, "url": "https://arxiv.org/abs/1908.09791"}, "MCUNet": {"title": "MCUNet: Tiny Deep Learning on IoT Devices", "year": 2020, "url": "https://arxiv.org/abs/2007.10319"}, "Kaplan scaling": {"title": "Scaling Laws for Neural Language Models", "year": 2020, "url": "https://arxiv.org/abs/2001.08361"}, "Chinchilla": {"title": "Training Compute-Optimal Large Language Models", "year": 2022, "url": 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"Grokking": {"title": "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets", "year": 2022, "url": "https://arxiv.org/abs/2201.02177"}, "PAC-Bayes bounds": {"title": "Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data", "year": 2017, "url": "https://arxiv.org/abs/1703.11008"}, "Double Descent": {"title": "Reconciling modern machine learning practice and the bias-variance trade-off", "year": 2019, "url": "https://arxiv.org/abs/1812.11118"}, "Neural Tangent Kernel": {"title": "Neural Tangent Kernel: Convergence and Generalization in Neural Networks", "year": 2018, "url": "https://arxiv.org/abs/1806.07572"}, "Mean-Field Theory": {"title": "A Mean Field View of the Landscape of Two-Layers Neural Networks", "year": 2018, "url": "https://arxiv.org/abs/1804.06561"}, "Implicit bias of GD": {"title": "The Implicit Bias of Gradient Descent on Separable Data", "year": 2018, "url": "https://arxiv.org/abs/1710.10345"}, "Neural Collapse": {"title": "Prevalence of Neural Collapse during the terminal phase of deep learning training", "year": 2020, "url": "https://arxiv.org/abs/2008.08186"}, "ICL as Gradient Descent": {"title": "Transformers learn in-context by gradient descent", "year": 2023, "url": "https://arxiv.org/abs/2212.07677"}, "Mean Teacher": {"title": "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results", "year": 2017, "url": "https://arxiv.org/abs/1703.01780"}, "FixMatch": {"title": "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence", "year": 2020, "url": "https://arxiv.org/abs/2001.07685"}, "Multiple Instance Learning": {"title": "Attention-based Deep Multiple Instance Learning", "year": 2018, "url": "https://arxiv.org/abs/1802.04712"}, "Co-teaching": {"title": "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels", "year": 2018, "url": "https://arxiv.org/abs/1804.06872"}, "DivideMix": {"title": "DivideMix: Learning with Noisy Labels as Semi-supervised Learning", "year": 2020, "url": "https://arxiv.org/abs/2002.07394"}, "Core-Set": {"title": "Active Learning for Convolutional Neural Networks: A Core-Set Approach", "year": 2018, "url": "https://arxiv.org/abs/1708.00489"}, "BADGE": {"title": "Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds", "year": 2020, "url": "https://arxiv.org/abs/1906.03671"}, "Noisy Student": {"title": "Self-training with Noisy Student improves ImageNet classification", "year": 2020, "url": "https://arxiv.org/abs/1911.04252"}, "Snorkel": {"title": "Snorkel: Rapid Training Data Creation with Weak Supervision", "year": 2017, "url": "https://arxiv.org/abs/1711.10160"}, "Curriculum Learning (Bengio)": {"title": "Curriculum Learning", "year": 2009, "url": "https://doi.org/10.1145/1553374.1553380"}, "GradNorm": {"title": "GradNorm: Gradient Normalization for Adaptive Loss 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Pruning": {"title": "Beyond neural scaling laws: beating power law scaling via data pruning", "year": 2022, "url": "https://arxiv.org/abs/2206.14486"}, "Dataset Distillation": {"title": "Dataset Distillation", "year": 2018, "url": "https://arxiv.org/abs/1811.10959"}, "Dataset Condensation": {"title": "Dataset Condensation with Gradient Matching", "year": 2021, "url": "https://arxiv.org/abs/2006.05929"}, "Mixup": {"title": "mixup: Beyond Empirical Risk Minimization", "year": 2018, "url": "https://arxiv.org/abs/1710.09412"}, "CutMix": {"title": "CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features", "year": 2019, "url": "https://arxiv.org/abs/1905.04899"}, "RandAugment": {"title": "RandAugment: Practical automated data augmentation with a reduced search space", "year": 2020, "url": "https://arxiv.org/abs/1909.13719"}, "Datasheets for Datasets": {"title": "Datasheets for Datasets", "year": 2021, "url": "https://arxiv.org/abs/1803.09010"}, "Snapshot Ensembles": {"title": "Snapshot Ensembles: Train 1, get M for free", "year": 2017, "url": "https://arxiv.org/abs/1704.00109"}, "Model Soups": {"title": "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time", "year": 2022, "url": "https://arxiv.org/abs/2203.05482"}, "Task Arithmetic": {"title": "Editing Models with Task Arithmetic", "year": 2023, "url": "https://arxiv.org/abs/2212.04089"}, "TIES-Merging": {"title": "TIES-Merging: Resolving Interference When Merging Models", "year": 2023, "url": "https://arxiv.org/abs/2306.01708"}, "Neural Module Networks": {"title": "Neural Module Networks", "year": 2016, "url": "https://arxiv.org/abs/1511.02799"}, "Net2Net": {"title": "Net2Net: Accelerating Learning via Knowledge Transfer", "year": 2016, "url": "https://arxiv.org/abs/1511.05641"}, "Git Re-Basin": {"title": "Git Re-Basin: Merging Models modulo Permutation Symmetries", "year": 2023, "url": "https://arxiv.org/abs/2209.04836"}, "ROME": {"title": "Locating and Editing Factual Associations in GPT", "year": 2022, "url": "https://arxiv.org/abs/2202.05262"}, "MEMIT": {"title": "Mass-Editing Memory in a Transformer", "year": 2023, "url": "https://arxiv.org/abs/2210.07229"}, "SISA": {"title": "Machine Unlearning", "year": 2021, "url": "https://arxiv.org/abs/1912.03817"}, "Gradient Ascent unlearning": {"title": "Knowledge Unlearning for Mitigating Privacy Risks in Language Models", "year": 2023, "url": "https://arxiv.org/abs/2210.01504"}, "VisProg": {"title": "Visual Programming: Compositional visual reasoning without training", "year": 2023, "url": "https://arxiv.org/abs/2211.11559"}, "ViperGPT": {"title": "ViperGPT: Visual Inference via Python Execution for Reasoning", "year": 2023, "url": "https://arxiv.org/abs/2303.08128"}, "HuggingGPT": {"title": "HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face", "year": 2023, "url": "https://arxiv.org/abs/2303.17580"}, "Frozen": {"title": "Multimodal Few-Shot Learning with Frozen Language Models", "year": 2021, "url": "https://arxiv.org/abs/2106.13884"}, "LiT": {"title": "LiT: Zero-Shot Transfer with Locked-image text Tuning", "year": 2022, "url": "https://arxiv.org/abs/2111.07991"}}}