社会层面 · accountability transparency and interpretability论文趋势 2023–2026

主要结论

• 社会层面 · accountability transparency and interpretability论文从 2023 年的 301 篇增长到 2026 年的 1,357 篇，三年间扩大到约 4.5 倍。
• NeurIPS 是发社会层面 · accountability transparency and interpretability论文最多的会议，2023–2026 年共 1,275 篇，占四个会议总量的 37%。
• 交叉方向里涨得最快的是社会层面 · privacy：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 93 篇。
• 在「对齐与负责任 AI」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的社会层面 · safety是 93 倍，社会层面 · accountability transparency and interpretability是 4.5 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A Unifying Perspective on Language Model Interpretability
• ICML 2026$\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
• ICLR 2026Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformers
• CVPR 2026Beyond Matching to Tiles Bridging Unaligned Aerial and Satellite Views
• NeurIPS 2025$\mathcal{X}^2$-DFD: A framework for e$\mathcal{X}$plainable and e$\mathcal{X}$tendable Deepfake Detection
• ICML 2025(How) Can Transformers Predict Pseudo-Random Numbers?
• ICLR 2025Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers
• CVPR 2025Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
• NeurIPS 20242D-OOB: Attributing Data Contribution Through Joint Valuation Framework
• ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
• ICLR 2024"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
• ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
• CVPR 2023Analyzing and Diagnosing Pose Estimation With Attributions
• NeurIPS 2026AdKnob: Ad Intensity Control and Labeling for LLM-Native Advertising
• ICML 2026$\texttt{ShaplEIG}$: Bayesian Experimental Design for Shapley Value Estimation
• ICLR 2026Bandits with Single-Peaked Preferences and Limited Resources
• CVPR 2026IEBGLAn Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and

全部社会层面 · accountability transparency and interpretability论文

共 3,411 篇，第 55 / 69 页

2701. ICLR 2024Vision Transformers Need Registers
2702. ICLR 2024What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity
2703. ICLR 2024What's In My Big Data?
2704. ICML 2024A connection between Tempering and Entropic Mirror Descent
2705. ICML 2024A Multimodal Automated Interpretability Agent
2706. ICML 2024A Persuasive Approach to Combating Misinformation
2707. ICML 2024A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation
2708. ICML 2024Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion
2709. ICML 2024Active Ranking and Matchmaking, with Perfect Matchings
2710. ICML 2024AI Alignment with Changing and Influenceable Reward Functions
2711. ICML 2024AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
2712. ICML 2024An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
2713. ICML 2024Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
2714. ICML 2024Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data
2715. ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
2716. ICML 2024Balanced Resonate-and-Fire Neurons
2717. ICML 2024Benchmarking Deletion Metrics with the Principled Explanations
2718. ICML 2024Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium
2719. ICML 2024Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses
2720. ICML 2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
2721. ICML 2024Can Machines Learn the True Probabilities?
2722. ICML 2024Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
2723. ICML 2024Clifford-Steerable Convolutional Neural Networks
2724. ICML 2024Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
2725. ICML 2024Codebook Features: Sparse and Discrete Interpretability for Neural Networks
2726. ICML 2024Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
2727. ICML 2024Compressing Large Language Models by Joint Sparsification and Quantization
2728. ICML 2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
2729. ICML 2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
2730. ICML 2024Contextual Feature Selection with Conditional Stochastic Gates
2731. ICML 2024Counterfactual Metarules for Local and Global Recourse
2732. ICML 2024Criterion Collapse and Loss Distribution Control
2733. ICML 2024Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization
2734. ICML 2024Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation
2735. ICML 2024Detecting and Identifying Selection Structure in Sequential Data
2736. ICML 2024Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
2737. ICML 2024DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
2738. ICML 2024Does Label Smoothing Help Deep Partial Label Learning?
2739. ICML 2024Don't trust your eyes: on the (un)reliability of feature visualizations
2740. ICML 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
2741. ICML 2024EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
2742. ICML 2024Enhancing Implicit Shape Generators Using Topological Regularizations
2743. ICML 2024Equivariant Frames and the Impossibility of Continuous Canonicalization
2744. ICML 2024Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
2745. ICML 2024Expand-and-Cluster: Parameter Recovery of Neural Networks
2746. ICML 2024Exploration and Anti-Exploration with Distributional Random Network Distillation
2747. ICML 2024Explorations of Self-Repair in Language Models
2748. ICML 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
2749. ICML 2024FESSNC: Fast Exponentially Stable and Safe Neural Controller
2750. ICML 2024From Neurons to Neutrons: A Case Study in Interpretability

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · accountability transparency and interpretability论文？

共 3,411 篇，按年份分别是 2023 年 301 篇、2024 年 589 篇、2025 年 1,164 篇、2026 年 1,357 篇。

NeurIPS 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

NeurIPS 2026 有 416 篇社会层面 · accountability transparency and interpretability论文，上一届是 522 篇。

ICLR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICLR 2026 有 292 篇社会层面 · accountability transparency and interpretability论文，上一届是 211 篇。

ICML 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICML 2026 有 541 篇社会层面 · accountability transparency and interpretability论文，上一届是 337 篇。

CVPR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

CVPR 2026 有 108 篇社会层面 · accountability transparency and interpretability论文，上一届是 94 篇。

哪个会议的社会层面 · accountability transparency and interpretability论文最多？

NeurIPS，2023–2026 年共 1,275 篇。