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@@ -58,3 +58,203 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+slides/slide5_POEM_-_Out-of-Distribution_Detection_with_Posterior_Sampling_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide60_Learning_Streaming_Video_Representation_via_Multitask_Training_ICCV_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide61_ACCELERATED_TRAINING_THROUGH_ITERATIVE_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide62_BOOSTER_-_T_ACKLING_HARMFUL_FINE_-TUNING_FOR_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide63_CHART_MOE_-_M_IXTURE_OF_DIVERSELY_ALIGNED_EX_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide64_CYBER_HOST_-_A_O_NE-STAGE_DIFFUSION_FRAMEWORK_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide65_DEPT_-_D_ECOUPLED_EMBEDDINGS_FOR_PRE_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide66_FLAT_REWARD_IN_POLICY_PARAMETER_SPACE_IMPLIES_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide67_GESUBNET_-_G_ENE_INTERACTION_INFERENCE_FOR_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide68_GRID_MIX_-_E_XPLORING_SPATIAL_MODULATION_FOR_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide69_IMPROVING_PROBABILISTIC_DIFFUSION_MODELS_WITH_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide6_Path-Gradient_Estimators_for_Continuous_Normalizing_Flows_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide70_IMPROVING_PROBABILISTIC_DIFFUSION_MODELS_WITH_2_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide71_Inference_Scaling_for_Long-Context_Retrieval_Augmented_Generation_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide72_KNOWING_YOUR_TARGET_-_TARGET_-AWARE_TRANSFORMER_MAKES_BETTER_SPATIO-T_EMPORAL_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide73_LEARNING_TO_DISCRETIZE_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide74_MEASURING_AND_ENHANCING_TRUSTWORTHINESS_OF_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide75_NEURAL_PLANE_-_S_TRUCTURED_3D_R_ECONSTRUCTION_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide76_Navigating_the_Digital_World_as_Humans_Do_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide77_OPEN_-VOCABULARY_OBJECT_DETECTION_VIA_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide78_Open-YOLO_3D_-_Towards_Fast_and_Accurate_Open-Vocabulary_3D_Instance_Segmentation_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide79_PROBABILISTIC_LEARNING_TO_DEFER_-_H_ANDLING_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide7_Privacy_for_Free_-_How_does_Dataset_Condensation_Help_Privacy_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide80_REPRESENTATION_ALIGNMENT_FOR_GENERATION_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide81_SD-L_ORA_-_S_CALABLE_DECOUPLED_LOW-RANK_ADAP_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide82_SPIDER_20_-_E_VALUATING_LANGUAGE_MODELS_ON_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide83_STANDARD_GAUSSIAN_PROCESS_IS_ALL_YOU_NEED_FOR_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide84_TIME_MIXER__-_A_G_ENERAL_TIME_SERIES_PATTERN_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide85_TOWARD_GUIDANCE_-F_REE_AR_V_ISUAL_GENERATION_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide86_UNLOCKING_STATE-TRACKING_IN_LINEAR_RNN_S_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide87_miniCTX_-_NEURAL_THEOREM_PROVING_WITH_LONG_-_CONTEXTS_ICLR_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide88_Can_MLLMs_Reason_in_Multimodality_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide89_Foundation_Model_Insights_and_a_Multi-Model_Approach_for_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide8_Rethinking_Image-Scaling_Attacks_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide90_Learning_with_Expected_Signatures_-_Theory_and_Applications_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide91_LoRA-One_-_One-Step_Full_Gradient_Could_Suffice_for_Fine-Tuning_Large_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide92_Model_Immunization_from_a_Condition_Number_Perspective_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide93_Position_-_Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide94_Sundial_-_A_Family_of_Highly_Capable_Time_Series_Foundation_Models_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide95_VideoRoPE_-_What_Makes_for_Good_Video_Rotary_Position_Embedding_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide96_Adaptive_Surrogate_Gradients_for_Sequential_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide97_Dynam3D_-_Dynamic_Layered_3D_Tokens_Empower_VLM_for_Vision-and-Language_Navigation_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide98_GNNXEMPLAR_-_Exemplars_to_Explanations_-_Natural_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide99_OpenHOI_-_Open-World_Hand-Object_Interaction_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text
+slides/slide9_RieszNet_and_ForestRiesz_-_Automatic_Debiased_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,197 @@
+---
+license: mit
+language:
+  - en
+tags:
+  - machine-learning
+  - academic-papers
+  - slides
+  - multimodal
+  - benchmark
+  - nlp
+  - computer-vision
+  - oral-presentations
+pretty_name: ArcBench ML Conference Oral Paper-Presentation Benchmark
+size_categories:
+  - n<1K
+dataset_info:
+  features:
+    - name: Paper Title
+      dtype: string
+    - name: Year
+      dtype: int32
+    - name: Conference
+      dtype: string
+    - name: Presentation Type
+      dtype: string
+    - name: Number of Figures
+      dtype: int32
+    - name: Number of Equations
+      dtype: int32
+    - name: Number of Tables
+      dtype: int32
+    - name: Appendix
+      dtype: string
+    - name: Slide Animations
+      dtype: string
+    - name: Character_Count
+      dtype: int32
+    - name: Number_of_Slides
+      dtype: int32
+    - name: Topics
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 100
+---
+
+# ArcBench: ML Conference Oral Paper-Presentation Benchmark
+
+A curated benchmark dataset of **100 oral presentation papers** from top-tier machine learning conferences (CVPR, ICCV, ICLR, ICML, NeurIPS), spanning 2022–2025. Each entry includes the full paper PDF, presentation slides PDF, and rich metadata.
+
+[![arXiv](https://img.shields.io/badge/arXiv-2411.17957-b31b1b.svg)](https://arxiv.org/abs/2411.17957)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://arcdeck.org/)
+[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/ArcDeck/ArcBench)
+
+---
+
+## Dataset Summary
+
+This benchmark is designed to support research on multimodal document understanding, slide generation, paper-to-slide alignment, and LLM evaluation tasks. Papers were selected from oral presentations only — the highest-quality subset of each conference — and filtered to ensure rich content (≥3 figures, ≥3 tables) and availability of both the original paper PDF and presentation slides.
+
+---
+
+## Dataset Structure
+
+### Files
+
+```
+EvaluationBenchmark/
+├── benchmark.csv          # Metadata for all 100 papers
+├── papers/                # 100 original full-length paper PDFs
+│   └── paper{i}_{Title}_{Conference}_{Year}.pdf
+└── slides/                # 100 presentation slide PDFs
+    └── slide{i}_{Title}_{Conference}_{Year}.pdf
+```
+
+### Metadata Fields (`benchmark.csv`)
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `Paper Title` | string | Full paper title |
+| `Year` | int | Publication year (2022–2025) |
+| `Conference` | string | Conference name (CVPR, ICCV, ICLR, ICML, NeurIPS) |
+| `Presentation Type` | string | Always `Oral` in this benchmark |
+| `Number of Figures` | int | Number of figures in the paper |
+| `Number of Equations` | int | Number of equations in the paper |
+| `Number of Tables` | int | Number of tables in the paper |
+| `Appendix` | string | Whether paper has an appendix (`Yes`/`No`) |
+| `Slide Animations` | string | Notes on slide animations, if any |
+| `Character_Count` | int | Total character count of the paper (extracted via PDF) |
+| `Number_of_Slides` | int | Number of pages/slides in the slide PDF |
+| `Topics` | string | Semicolon-separated LLM-extracted research topics |
+
+### Naming Convention
+
+Files are named as `{type}{index}_{CleanTitle}_{Conference}_{Year}.pdf` where:
+- `index` is 0-based, consistent across `papers/` and `slides/` for matched pairs
+- `CleanTitle` has special characters removed and spaces replaced by underscores (max 100 chars)
+
+---
+
+## Dataset Statistics
+
+### Distribution by Conference
+
+| Conference | Papers |
+|------------|--------|
+| ICML       | 51     |
+| ICLR       | 31     |
+| NeurIPS    | 12     |
+| ICCV       |  4     |
+| CVPR       |  2     |
+
+### Distribution by Year
+
+| Year | Papers |
+|------|--------|
+| 2022 | 15     |
+| 2023 | 15     |
+| 2024 | 26     |
+| 2025 | 44     |
+
+### Content Statistics
+
+| Metric | Mean | Min | Max |
+|--------|------|-----|-----|
+| Figures per paper | 6.0 | 3 | 18 |
+| Tables per paper | 5.3 | 3 | — |
+| Slides per paper | 27.5 | 8 | 85 |
+| Characters per paper | 50,411 | — | — |
+
+- **92%** of papers include an appendix
+- **100%** are oral presentations
+
+### Top Research Topics
+
+Extracted via GPT-4o-mini from paper abstracts:
+
+> Contrastive Learning · Graph Neural Networks · Causal Inference · Multimodal Large Language Models · Federated Learning · Sampling Efficiency · Reinforcement Learning · Diffusion Models · Self-Supervised Learning · Vision-Language Models
+
+---
+
+## Selection Criteria
+
+Papers were selected using the following filters applied to a broader 994-paper dataset:
+
+- **Presentation type:** Oral only
+- **Minimum figures:** ≥ 3
+- **Minimum tables:** ≥ 3
+- **Original paper available:** Must have the full (non-anonymized) version
+- **Balanced sampling:** Proportional stratified sampling across year × conference to reach exactly 100 papers
+
+---
+
+## Intended Uses
+
+This dataset is suited for:
+
+- **Slide generation / paper-to-slide summarization**: Given `papers/`, generate slides comparable to `slides/`
+- **Slide-grounded QA**: Answer questions about a paper using its slides as context
+- **Cross-modal retrieval**: Match papers to their corresponding slides
+- **LLM evaluation**: Benchmark LLM understanding of dense scientific documents
+- **Multimodal document analysis**: Study relationships between figures, tables, equations, and slide content
+
+---
+
+## Source
+
+Papers were collected from official proceedings of:
+- [ICML](https://icml.cc) (2022–2025)
+- [ICLR](https://iclr.cc) (2024–2025)
+- [NeurIPS](https://neurips.cc) (2022–2025)
+- [CVPR](https://cvpr.thecvf.com) (2024–2025)
+- [ICCV](https://iccv2023.thecvf.com) (2025)
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{ozden2026arcdeck,
+  title     = {Narrative-Driven Paper-to-Slide Generation via ArcDeck},
+  author    = {Ozden, Tarik Can and VS, Sachidanand and Horoz, Furkan
+               and Kara, Ozgur and Kim, Junho and Rehg, James M.},
+  journal   = {arXiv preprint arXiv:2604.11969},
+  year      = {2026}
+}
+```
+
+---
+
+## License
+
+MIT.
+Individual paper and slide PDFs remain under their original authors' copyright. Please consult each paper's license before redistribution.

benchmark.csv

@@ -0,0 +1,101 @@
+Paper Title,Year,Conference,Presentation Type,Number of Figures,Number of Equations,Number of Tables,Appendix,Slide Animations,Character_Count,Number_of_Slides,Topics
+3DLinker - An E(3) Equivariant Variational Autoencoder,2022,ICML,Oral,4,,3,Yes,,45879,19,Molecular Linker Design;E(3) Equivariant Graph Variational Autoencoder;Conditional Generative Models;3D Molecular Structure Prediction
+"Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness",2022,ICML,Oral,4,,3,Yes,,44347,47,Counterfactual Inference;Data Integration;Fair Representation Learning;Batch Effect Correction
+Correct-N-Contrast - A Contrastive Approach for Improving Robustness to Spurious Correlations,2022,ICML,Oral,7,,4,Yes,,44964,78,Robust Machine Learning;Spurious Correlations;Contrastive Learning;Representation Alignment
+Learning inverse folding from millions of predicted structures,2022,ICML,Oral,7,,3,Yes,,42125,18,Inverse Folding Prediction;Protein Structure Prediction;Generative Models for Protein Design;Sequence-to-Sequence Learning in Protein Engineering
+Monarch - Expressive Structured Matrices for Efficient and Accurate Training,2022,ICML,Oral,5,,8,Yes,,43885,44,Structured Matrices;Sparse Training;Efficient Neural Network Training;Matrix Approximation Techniques
+POEM - Out-of-Distribution Detection with Posterior Sampling,2022,ICML,Oral,3,,3,Yes,,54444,34,Out-of-Distribution Detection;Posterior Sampling;Outlier Detection;Neural Network Regularization
+Path-Gradient Estimators for Continuous Normalizing Flows,2022,ICML,Oral,5,,3,Yes,,39565,29,Path-Gradient Estimators;Continuous Normalizing Flows;Variational Inference;High-Dimensional Systems
+Privacy for Free - How does Dataset Condensation Help Privacy,2022,ICML,Oral,8,,3,Yes,,53800,24,Dataset Condensation;Differential Privacy;Membership Inference Attacks;Data Privacy in Machine Learning
+Rethinking Image-Scaling Attacks,2022,ICML,Oral,9,,4,Yes,,47649,44,Image Scaling Algorithms;Adversarial Attacks;Machine Learning Vulnerabilities;Decision-Based Black-Box Attacks
+RieszNet and ForestRiesz - Automatic Debiased,2022,ICML,Oral,3,,4,Yes,,46414,33,Automatic Debiasing;Riesz Representation;Neural Networks;Random Forests
+To Smooth or Not When Label Smoothing Meets Noisy Labels,2022,ICML,Oral,5,,6,Yes,,46876,27,Label Smoothing;Noisy Label Learning;Regularization Techniques;Negative Label Smoothing
+Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning,2022,ICML,Oral,8,,4,No,,51214,20,Topology-Aware Network Pruning;Graph Neural Networks;Reinforcement Learning;Model Compression
+Understanding Dataset Difficulty with V-Usable Information,2022,ICML,Oral,9,,4,Yes,,58804,32,Dataset Difficulty Estimation;V-Usable Information;Pointwise V-Information;NLP Benchmark Analysis
+Unified Scaling Laws for Routed Language Models,2022,ICML,Oral,13,,6,Yes,,45020,84,Scaling Laws in Language Models;Routing Networks;Neural Network Architecture Evaluation;Effective Parameter Count
+"data2vec - A General Framework for Self-supervised Learning in Speech, Vision and Language",2022,ICML,Oral,3,,6,Yes,,47928,14,Self-supervised Learning;Multimodal Learning;Contextualized Representations;Speech Recognition
+Adversarial Example Does Good - Preventing Painting Imitation from,2023,ICML,Oral,5,,4,Yes,,43771,12,Adversarial Training;Diffusion Models;Copyright Protection in AI Art;Image Synthesis
+Audio Pre-Training with Acoustic Tokenizers,2023,ICML,Oral,4,,3,Yes,,49406,28,Self-Supervised Learning for Audio;Acoustic Tokenization;Audio Representation Learning;Audio Classification Benchmarking
+Bidirectional Adaptation for Robust Semi-Supervised Learning,2023,ICML,Oral,3,,3,Yes,,52501,8,Robust Semi-Supervised Learning;Distribution Adaptation;Debiased Pseudo-Labeling;Theoretical Framework for SSL
+Evaluating Self-Supervised Learning via Risk Decomposition,2023,ICML,Oral,13,,4,Yes,,58816,28,Self-Supervised Learning;Risk Decomposition;Error Analysis in Representation Learning;Evaluation Metrics for SSL Models
+Fast Inference from Transformers via Speculative Decoding,2023,ICML,Oral,5,,3,Yes,,34452,14,Speculative Decoding;Parallel Inference;Autoregressive Models
+Instant Soup - Cheap Pruning Ensembles in A Single Pass Can,2023,ICML,Oral,3,,7,Yes,,46357,8,Pruning Ensembles;Lottery Ticket Hypothesis;Model Fine-Tuning;Large Pre-trained Transformers
+ODS - Test-Time Adaptation in the Presence of Open-World Data Shift,2023,ICML,Oral,7,,7,Yes,,43543,21,Test-Time Adaptation;Open-World Data Shift;Covariate and Label Distribution Shift
+Pre-training for Speech Translation - CTC Meets Optimal Transport,2023,ICML,Oral,5,,10,Yes,,48994,21,Speech-to-Text Translation;Connectionist Temporal Classification;Optimal Transport;Siamese Neural Networks
+Refining Generative Process with Discriminator Guidance,2023,ICML,Oral,15,,8,Yes,,45867,19,Score-based Diffusion Models;Discriminator-guided Learning;Generative Adversarial Networks (GANs) Alternative Approaches;Image Generation Techniques
+Subequivariant Graph Reinforcement Learning in 3D Environments,2023,ICML,Oral,8,,5,Yes,,47869,21,Morphology-Agnostic Reinforcement Learning;Subequivariant Graph Neural Networks;3D Environment Exploration;Policy Optimization via Geometric Symmetry
+Which Features are Learnt by Contrastive Learning,2023,ICML,Oral,6,,5,Yes,,54697,62,Contrastive Learning;Representation Learning;Feature Suppression;Class Collapse
+Causal normalizing flows - from theory to practice,2023,NeurIPS,Oral,5,,3,Yes,,52327,67,Causal Inference;Normalizing Flows;Autoregressive Models;Counterfactual Reasoning
+Conformal Meta-learners for Predictive Inference of,2023,NeurIPS,Oral,5,,3,Yes,,53153,25,Individual Treatment Effects;Conformal Prediction;Meta-Learning;Causal Inference
+Learning Linear Causal Representations from Interventions,2023,NeurIPS,Oral,3,,3,Yes,,62186,31,Causal Inference;Identification of Latent Variables;Contrastive Learning;High-Dimensional Geometry in Causal Learning
+QL ORA - Efficient Finetuning of Quantized LLMs,2023,NeurIPS,Oral,3,,8,Yes,,75353,20,Efficient Finetuning of Quantized Language Models;Low Rank Adaptation (LoRA);Memory Optimization Techniques;Chatbot Performance Evaluation
+Learning to Segment Referred Objects from Narrated Egocentric Videos,2024,CVPR,Oral,4,,3,No,,55536,17,Weakly-Supervised Video Object Segmentation;Vision-Language Models;Contrastive Learning;Egocentric Video Analysis
+CAMERAS AS RAYS - POSE ESTIMATION VIA RAY DIFFUSION,2024,ICLR,Oral,7,,3,Yes,,40716,21,Pose Estimation;Ray Diffusion;3D Reconstruction;Denoising Diffusion Models
+CLIM ODE - C LIMATE AND WEATHER FORECASTING,2024,ICLR,Oral,8,,3,Yes,,35588,35,Physics-Informed Neural Networks;Weather Forecasting;Uncertainty Quantification;Spatiotemporal Modeling
+LESS IS MORE - F EWER INTERPRETABLE REGION VIA,2024,ICLR,Oral,3,,4,Yes,,47332,18,Image Attribution;Submodular Optimization;Model Interpretability
+METAGPT - M ETA PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK,2024,ICLR,Oral,5,,6,Yes,,46728,17,Meta-Programming;Multi-Agent Systems;Large Language Models;Automated Problem Solving
+"A Touch, Vision, and Language Dataset for Multimodal Alignment",2024,ICML,Oral,6,,4,Yes,,42012,48,Multimodal Representation Learning;Tactile Data Annotation;Vision-Touch Language Alignment;Generative Language Models
+APT - Adaptive Pruning and Tuning Pretrained Language Models for,2024,ICML,Oral,3,,7,Yes,,48883,20,Adaptive Pruning;Efficient Fine-Tuning;Parameter-Efficient Models;Language Model Optimization
+Arrows_of_Time_for_Large_Language_Models,2024,ICML,Oral,4,,5,Yes,,52767,85,Autoregressive Language Modeling;Time Asymmetry in Probabilistic Models;Information Theory in AI Systems;Sparse Representation in Neural Networks
+Bottleneck-Minimal Indexing for Generative Document Retrieval,2024,ICML,Oral,6,,4,Yes,,45364,24,Generative Document Retrieval;Information-Theoretic Approaches;Rate-Distortion Theory;Neural Autoregressive Models
+Candidate Pseudolabel Learning - Enhancing Vision-Language Models by,2024,ICML,Oral,7,,6,Yes,,48882,15,Candidate Pseudolabel Learning;Vision-Language Models;Unlabeled Data Fine-tuning;Prompt Tuning
+Challenges in Training PINNs - A Loss Landscape Perspective,2024,ICML,Oral,8,,3,Yes,,53420,30,Physics-Informed Neural Networks;Loss Landscape Optimization;Gradient-Based Optimization Methods;Partial Differential Equations
+Data-free Neural Representation Compression,2024,ICML,Oral,3,,5,Yes,,47302,18,Riemannian Geometry in Neural Networks;Data-free Neural Network Compression;Dynamic Systems and Neural Representation;Neuronal Interaction Modeling
+ExCP - Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking,2024,ICML,Oral,5,,7,Yes,,42889,14,Checkpoint Compression;Weight-Momentum Optimization;Non-Uniform Quantization;Sparse Information Extraction
+Expressivity and Generalization - Fragment-Biases for Molecular GNNs,2024,ICML,Oral,6,,7,Yes,,50175,23,Fragment-Biased Graph Neural Networks;Theoretic Expressivity Analysis;Molecular Property Prediction;Generalization in Machine Learning
+Listenable Maps for Zero-Shot Audio Classifiers,2024,ICML,Oral,5,,3,Yes,,45423,42,Explainable AI;Zero-Shot Learning;Audio Classification;Post-Hoc Explanation Methods
+MLLM-as-a-Judge - Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark,2024,ICML,Oral,9,,8,Yes,,61182,33,Multimodal Large Language Models;Benchmark Development;Judgment Biases in AI;Evaluation Metrics for AI Systems
+MorphGrower - A Synchronized Layer-by-layer Growing Approach for,2024,ICML,Oral,5,,3,Yes,,49482,19,Neuronal Morphology Generation;Synchronized Layer-by-layer Growth;Topological Validity in Morphologies;Electrophysiological Response Simulation
+Position - Rethinking Post-Hoc Search-Based Neural Approaches for Solving,2024,ICML,Oral,4,,3,Yes,,45092,25,Heatmap Generation for Optimization;Monte Carlo Tree Search;Traveling Salesman Problem;Combinatorial Optimization Methods
+SparseTSF - Modeling Long-term Time Series Forecasting with 1k Parameters,2024,ICML,Oral,4,,7,Yes,,49044,18,Long-term Time Series Forecasting;Cross-Period Sparse Forecasting;Parameter Efficiency in Machine Learning Models;Generalization in Low-Resource Scenarios
+Towards_Optimal_Adversarial_Robust_Q-learning_with_Bellman_Infinity-error,2024,ICML,Oral,5,,3,Yes,,54693,30,Adversarial Robustness in Reinforcement Learning;Optimal Robust Policy in Markov Decision Processes;Bellman Error Minimization Techniques;Deep Q-Networks Training Methods
+Unified_Training_of_Universal_Time_Series_Forecasting_Transformers,2024,ICML,Oral,6,,7,Yes,,57564,39,Universal Time Series Forecasting;Transformer Architectures;Cross-Frequency Learning;Multivariate Time Series Analysis
+Video-of-Thought - Step-by-Step Video Reasoning from Perception to Cognition,2024,ICML,Oral,8,,4,Yes,,59901,27,Video Understanding;Spatial-Temporal Reasoning;Multimodal Large Language Models;Cognitive Video Comprehension
+AgentBoard - An Analytical Evaluation Board of,2024,NeurIPS,Oral,6,,7,Yes,,59530,62,Evaluation Frameworks for Large Language Models;Multi-Turn Interaction in AI Agents;Benchmarking of Agent Performance;Interpretable AI
+DapperFL - Domain Adaptive Federated Learning with,2024,NeurIPS,Oral,4,,3,Yes,,52291,29,Federated Learning;Domain Adaptation;Model Pruning;Edge Computing
+"Decompose, Analyze and Rethink",2024,NeurIPS,Oral,5,,4,Yes,,53419,12,Natural Language Processing;Reasoning in AI;Tree-based Question Decomposition;Large Language Models
+You Only Cache Once - Decoder-Decoder Architectures for Language Models,2024,NeurIPS,Oral,10,,5,Yes,,44856,12,Decoder-Decoder Architectures;Key-Value Caching Optimization;Transformer Model Efficiency;Global Attention Mechanisms
+Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning,2025,CVPR,Oral,8,,7,No,,51814,10,Federated Learning;Data Heterogeneity;Geometric Distribution Alignment;Sample Generation Techniques
+Deterministic Object Pose Confidence Region Estimation,2025,ICCV,Oral,6,,6,No,,44266,27,6D Pose Estimation;Uncertainty Quantification;Deterministic Estimation Methods;Conformal Prediction
+Diffusion Image Prior,2025,ICCV,Oral,7,,6,No,,35462,22,Diffusion Models;Image Restoration;Blind Image Restoration;Artifact Removal
+Diving into the Fusion of Monocular Priors for Generalized Stereo Matching,2025,ICCV,Oral,4,,7,No,,48476,35,Stereo Matching;Monocular Depth Estimation;Depth Map Fusion;Ill-posed Problem Handling
+Learning Streaming Video Representation via Multitask Training,2025,ICCV,Oral,3,,6,No,,62845,19,Streaming Video Representation;Multitask Learning;Online Action Detection;Video Question Answering
+ACCELERATED TRAINING THROUGH ITERATIVE,2025,ICLR,Oral,6,,4,Yes,,44150,14,Highway Backpropagation;Residual Networks;Gradient Propagation Algorithms;Deep Learning Optimization
+BOOSTER - T ACKLING HARMFUL FINE -TUNING FOR,2025,ICLR,Oral,3,,9,Yes,,61851,16,Harmful Fine-Tuning Attacks;Large Language Model Alignment;Loss Regularization Techniques
+CHART MOE - M IXTURE OF DIVERSELY ALIGNED EX,2025,ICLR,Oral,11,,8,Yes,,58200,13,Chart Understanding;Mixture of Experts (MoE);Multimodal Large Language Models (MLLMs);Dataset Creation for Chart Analysis
+CYBER HOST - A O NE-STAGE DIFFUSION FRAMEWORK,2025,ICLR,Oral,6,,3,Yes,,49641,21,Audio-Driven Talking Body Generation;Diffusion-Based Video Generation;Human Animation Synthesis;Region Attention Module in Animation
+DEPT - D ECOUPLED EMBEDDINGS FOR PRE,2025,ICLR,Oral,5,,13,Yes,,67319,23,Decoupled Embeddings;Federated Learning;Language Model Pre-training;Communication-Efficient Training
+FLAT REWARD IN POLICY PARAMETER SPACE IMPLIES,2025,ICLR,Oral,7,,3,Yes,,48254,30,Flat Reward Landscapes;Robustness in Reinforcement Learning;Policy Parameter Space Analysis;Generalization in Deep Neural Networks
+GESUBNET - G ENE INTERACTION INFERENCE FOR,2025,ICLR,Oral,6,,4,Yes,,54038,21,Gene Interaction Inference;Disease Subtype Network Generation;Graph Neural Networks;Representation Learning
+GRID MIX - E XPLORING SPATIAL MODULATION FOR,2025,ICLR,Oral,4,,6,Yes,,50700,20,Spatial Modulation;Neural Fields;Partial Differential Equations (PDE) Modeling;Domain Augmentation
+IMPROVING PROBABILISTIC DIFFUSION MODELS WITH,2025,ICLR,Oral,4,,8,Yes,,51204,43,Probabilistic Diffusion Models;Optimal Covariance Matching;Denoising Distribution;Sampling Efficiency
+IMPROVING PROBABILISTIC DIFFUSION MODELS WITH (2),2025,ICLR,Oral,4,,8,Yes,,51204,17,Probabilistic Diffusion Models;Diagonal Covariance Matching;Optimal Covariance Matching;Sampling Efficiency
+Inference_Scaling_for_Long-Context_Retrieval_Augmented_Generation,2025,ICLR,Oral,8,,4,Yes,,46464,38,Long-Context Large Language Models;Retrieval Augmented Generation;Inference Scaling Laws;Optimal Computation Allocation
+KNOWING YOUR TARGET - TARGET -AWARE TRANSFORMER MAKES BETTER SPATIO-T EMPORAL,2025,ICLR,Oral,6,,10,Yes,,52853,17,Spatio-Temporal Video Grounding;Target-Aware Transformers;Multimodal Feature Interactions;Object Query Initialization
+LEARNING TO DISCRETIZE,2025,ICLR,Oral,6,,8,Yes,,49108,29,Diffusion Probabilistic Models;Sampling Efficiency;Neural Function Evaluations;Generative Model Optimization
+MEASURING AND ENHANCING TRUSTWORTHINESS OF,2025,ICLR,Oral,3,,10,Yes,,65340,39,Trustworthiness Evaluation in LLMs;Retrieval-Augmented Generation (RAG);Prompting Methods for LLMs;Model Alignment Techniques
+NEURAL PLANE - S TRUCTURED 3D R ECONSTRUCTION,2025,ICLR,Oral,7,,3,Yes,,48329,16,3D Plane Reconstruction;Neural Fields;Self-Supervised Learning;Semantic Segmentation
+Navigating the Digital World as Humans Do,2025,ICLR,Oral,5,,9,Yes,,61854,26,Visual Grounding;Multimodal Learning;Graphical User Interface (GUI) Agents;Synthetic Data Generation
+OPEN -VOCABULARY OBJECT DETECTION VIA,2025,ICLR,Oral,7,,6,Yes,,48188,12,Open-Vocabulary Object Detection;Knowledge Distillation;Vision and Language Integration;Transfer Learning
+Open-YOLO 3D - Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation,2025,ICLR,Oral,4,,5,Yes,,38184,17,Open-Vocabulary 3D Instance Segmentation;2D Object Detection;Multi-View Image Processing;Real-Time Inference Techniques
+PROBABILISTIC LEARNING TO DEFER - H ANDLING,2025,ICLR,Oral,5,,3,Yes,,47151,47,Learning to Defer;Probabilistic Modelling;Workload Distribution in Human-AI Cooperation;Handling Missing Annotations
+REPRESENTATION ALIGNMENT FOR GENERATION,2025,ICLR,Oral,8,,5,Yes,,61444,19,Representation Alignment;Denoising Diffusion Models;Visual Representation Learning;Training Efficiency in Generative Models
+SD-L ORA - S CALABLE DECOUPLED LOW-RANK ADAP,2025,ICLR,Oral,5,,5,Yes,,46379,28,Class Incremental Learning;Low-Rank Adaptation;Scalable Machine Learning;Continual Learning
+SPIDER 2.0 - E VALUATING LANGUAGE MODELS ON,2025,ICLR,Oral,18,,18,Yes,,58210,23,Text-to-SQL Transformation;SQL Query Generation;Model Evaluation Frameworks;Enterprise Database Systems
+STANDARD GAUSSIAN PROCESS IS ALL YOU NEED FOR,2025,ICLR,Oral,5,,4,Yes,,44476,24,Bayesian Optimization;Gaussian Processes;Matérn Kernels;High-Dimensional Optimization
+TIME MIXER ++ - A G ENERAL TIME SERIES PATTERN,2025,ICLR,Oral,5,,10,Yes,,59545,18,Time Series Analysis;Pattern Extraction;Multi-Resolution Time Imaging;Anomaly Detection
+TOWARD GUIDANCE -F REE AR V ISUAL GENERATION,2025,ICLR,Oral,8,,3,Yes,,60033,26,Classifier-Free Guidance;Autoregressive Visual Generation;Contrastive Learning;Multi-Modal Alignment
+UNLOCKING STATE-TRACKING IN LINEAR RNN S,2025,ICLR,Oral,9,,5,Yes,,56524,22,Linear Recurrent Neural Networks;State-Tracking in Neural Networks;Eigenvalue Analysis in Neural Networks;Language Modeling
+miniCTX - NEURAL THEOREM PROVING WITH (LONG -) CONTEXTS,2025,ICLR,Oral,5,,5,Yes,,49565,26,Neural Theorem Proving;Contextual Reasoning;Interactive Theorem Provers;Fine-Tuning Language Models
+Can MLLMs Reason in Multimodality,2025,ICML,Oral,8,,3,Yes,,45918,45,Multimodal Reasoning;Benchmark Development;Cross-Modal Reasoning Tasks;Large Language Models Evaluation
+Foundation Model Insights and a Multi-Model Approach for,2025,ICML,Oral,4,,3,Yes,,50190,36,One-Shot Subset Selection;Foundation Models;Fine-Grained Image Datasets;Data Efficiency in Deep Learning
+Learning with Expected Signatures - Theory and Applications,2025,ICML,Oral,3,,3,Yes,,46366,13,Expected Signatures;Time Series Analysis;Martingale Processes;Probabilistic Machine Learning
+LoRA-One - One-Step Full Gradient Could Suffice for Fine-Tuning Large,2025,ICML,Oral,5,,5,Yes,,45295,24,Low-Rank Adaptation;Gradient Descent Optimization;Fine-Tuning Large Language Models;Theory-Driven Algorithm Design
+Model_Immunization_from_a_Condition_Number_Perspective,2025,ICML,Oral,3,,3,Yes,,42978,20,Model Immunization;Condition Number Analysis;Hessian Matrix Regularization;Non-Harmful Task Retention
+Position - Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance,2025,ICML,Oral,9,,4,Yes,,44267,9,Model Licensing Practices;Legal Compliance in AI;Standardization of ML Licenses;Risks of License Noncompliance
+Sundial - A Family of Highly Capable Time Series Foundation Models,2025,ICML,Oral,10,,7,Yes,,57288,27,Time Series Forecasting;Transformers in Time Series;Generative Forecasting Models;Probabilistic Prediction Techniques
+VideoRoPE - What Makes for Good Video Rotary Position Embedding,2025,ICML,Oral,7,,5,Yes,,48167,17,Rotary Position Embedding;Spatio-Temporal Analysis;Video Retrieval;Video Understanding
+Adaptive Surrogate Gradients for Sequential,2025,NeurIPS,Oral,5,,7,No,,50102,60,Spiking Neural Networks;Surrogate Gradient Optimization;Reinforcement Learning;Neuromorphic Computing
+Dynam3D - Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation,2025,NeurIPS,Oral,4,,6,Yes,,49369,13,Vision-and-Language Navigation;3D Object Recognition;Dynamic 3D Representation;Long-Term Environmental Memory
+GNNXEMPLAR - Exemplars to Explanations - Natural,2025,NeurIPS,Oral,11,,3,Yes,,70794,41,Graph Neural Networks;Global Explainability;Natural Language Processing;Exemplar Selection
+OpenHOI - Open-World Hand-Object Interaction,2025,NeurIPS,Oral,3,,5,Yes,,44988,20,Open-World Hand-Object Interaction;Multimodal Large Language Models;Affordance-driven Diffusion Models;Complex Language Instruction Decomposition

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