惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Privacy International News Feed
D
Docker
WordPress大学
WordPress大学
G
Google Developers Blog
小众软件
小众软件
Stack Overflow Blog
Stack Overflow Blog
MyScale Blog
MyScale Blog
S
Security Archives - TechRepublic
S
SegmentFault 最新的问题
宝玉的分享
宝玉的分享
爱范儿
爱范儿
Application and Cybersecurity Blog
Application and Cybersecurity Blog
Google DeepMind News
Google DeepMind News
F
Full Disclosure
S
Secure Thoughts
S
Security @ Cisco Blogs
Recent Announcements
Recent Announcements
W
WeLiveSecurity
Schneier on Security
Schneier on Security
AWS News Blog
AWS News Blog
T
Tenable Blog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
U
Unit 42
Project Zero
Project Zero
V
V2EX
T
The Blog of Author Tim Ferriss
T
Tailwind CSS Blog
Spread Privacy
Spread Privacy
C
CERT Recently Published Vulnerability Notes
Webroot Blog
Webroot Blog
The Last Watchdog
The Last Watchdog
B
Blog
K
Kaspersky official blog
云风的 BLOG
云风的 BLOG
N
News and Events Feed by Topic
J
Java Code Geeks
阮一峰的网络日志
阮一峰的网络日志
美
美团技术团队
I
Intezer
雷峰网
雷峰网
GbyAI
GbyAI
罗
罗磊的独立博客
Jina AI
Jina AI
Help Net Security
Help Net Security
A
Arctic Wolf
腾
腾讯CDC
H
Heimdal Security Blog
V
Visual Studio Blog
TaoSecurity Blog
TaoSecurity Blog
Last Week in AI
Last Week in AI

cs.LG updates on arXiv.org

Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning Does Dimensionality Reduction via Random Projections Preserve Landscape Features? Analog Optical Inference on Million-Record Mortgage Data ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection Context Sensitivity Improves Human-Machine Visual Alignment Artificial intelligence application in lymphoma diagnosis with Vision Transformer using weakly supervised training Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery Rethinking Uncertainty in Segmentation: From Estimation to Decision A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks Spectral Entropy Collapse as a Phase Transition in Delayed Generalisation: An Interventional and Predictive Framework for Grokkin LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling Evaluating Cooperation in LLM Social Groups through Elected Leadership Towards Autonomous Mechanistic Reasoning in Virtual Cells Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification A Triadic Suffix Tokenization Scheme for Numerical Reasoning Not All Forgetting Is Equal: Architecture-Dependent Retention Dynamics in Fine-Tuned Image Classifiers Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference From Attribution to Action: A Human-Centered Application of Activation Steering THEIA: Learning Complete Kleene Three-Valued Logic in a Pure-Neural Modular Architecture Cost-optimal Sequential Testing via Doubly Robust Q-learning Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net A Faster Path to Continual Learning Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR Optimal Stability of KL Divergence under Gaussian Perturbations Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Policy-Aware Design of Large-Scale Factorial Experiments Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective A Quantitative Definition of Intelligence SpectralLoRA: Is Low-Frequency Structure Sufficient for LoRA Adaptation? A Spectral Analysis of Weight Updates A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense The Amazing Agent Race: Strong Tool Users, Weak Navigators MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions COMPOSITE-Stem SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories PhysInOne: Visual Physics Learning and Reasoning in One Suite FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval Detecting Diffusion-generated Images via Dynamic Assembly Forests PDE-regularized Dynamics-informed Diffusion with Uncertainty-aware Filtering for Long-Horizon Dynamics Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection Regime-Conditional Retrieval: Theory and a Transferable Router for Two-Hop QA Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection Hypergraph Neural Networks Accelerate MUS Enumeration ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout
Towards learning to explain with concept bottleneck models: mitigating information leakage
Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni, Manuela · 2022-11-08 · via cs.LG updates on arXiv.org

Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction. Model trust issues emerge in this paradigm when soft concept labels are used: it has previously been observed that extra information about the data distribution leaks into the concept predictions. In this work we show how Monte-Carlo Dropout can be used to attain soft concept predictions that do not contain leaked information.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。