Variational Regularization imposes an adaptive information bottleneck on noisy intermediate features in DP3-UNet and DP3-DiT policies, consistently raising task success rates on RoboTwin2.0, Adroit, and MetaWorld while achieving new state-of-the-art results.
Understanding Neural Networks through Representation Erasure
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model's decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.
representative citing papers
BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
SkillRet benchmark shows fine-tuned retrievers improve NDCG@10 by 13+ points over prior models on large-scale skill retrieval for LLM agents.
Caption Bottleneck Models use LMM-generated image captions as the sole input to a text classifier, creating leakage-free interpretable models that discover dataset-specific concepts without predefined lists or manual labels.
Knot estimates QA model sensitivity to candidate knowledge via subset counterfactual training and latent factor coverage, yielding unit rankings that outperform baselines without extra model calls.
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
NTM and stack-augmented networks both generalize on sequence reversal but only the stack model succeeds on arithmetic expressions by monitoring different input categories and applying distinct memory-update policies.
Compares LIME, input perturbation and attention for explaining QA on KB+text; proposes automatic evaluation paradigm and finds input perturbation superior in both automatic and human studies.
citing papers explorer
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Information Filtering via Variational Regularization for Robot Manipulation
Variational Regularization imposes an adaptive information bottleneck on noisy intermediate features in DP3-UNet and DP3-DiT policies, consistently raising task success rates on RoboTwin2.0, Adroit, and MetaWorld while achieving new state-of-the-art results.
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Language Models as Knowledge Bases?
BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
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SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents
SkillRet benchmark shows fine-tuned retrievers improve NDCG@10 by 13+ points over prior models on large-scale skill retrieval for LLM agents.
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Caption Bottleneck Models
Caption Bottleneck Models use LMM-generated image captions as the sole input to a text classifier, creating leakage-free interpretable models that discover dataset-specific concepts without predefined lists or manual labels.
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Knowledge Dependency Estimation for Reliable Question Answering
Knot estimates QA model sensitivity to candidate knowledge via subset counterfactual training and latent factor coverage, yielding unit rankings that outperform baselines without extra model calls.
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UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
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Understanding Memory Modules on Learning Simple Algorithms
NTM and stack-augmented networks both generalize on sequence reversal but only the stack model succeeds on arithmetic expressions by monitoring different input categories and applying distinct memory-update policies.
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Interpretable Question Answering on Knowledge Bases and Text
Compares LIME, input perturbation and attention for explaining QA on KB+text; proposes automatic evaluation paradigm and finds input perturbation superior in both automatic and human studies.