Analysis of Interactions
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SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Announce Type: cross Abstract: Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their functional form, or visualize only restricted interaction types. We propose Surrogate-based Analysis of Interactions via Local effect Smooths (SAILS), a model-agnostic framework that analyzes pairwise interactions through interpretable generalized additive model (GAM) surrogates fitted...
Annotation of Positive vs Negative User Interactions for Social Sign Prediction
Announce Type: new Abstract: Inferring the sign of social relationships from online interactions is a fundamental challenge in social network analysis. Existing approaches typically rely on sentiment analysis to label individual interactions as positive or negative, then aggregate these labels to assign a sign to the relationship.
Social Caption: Evaluating Social Understanding in Multimodal Models
arXiv:2601.14569v2 Announce Type: replace Abstract: Social understanding abilities are crucial for multimodal large language models (MLLMs) to interpret human social interactions. We introduce SOCIAL CAPTION, a framework grounded in interaction theory to evaluate social understanding abilities of MLLMs along three dimensions: Social Inference (SI), the ability to make accurate inferences about interactions; Holistic Social Analysis (HSA), the ability to generate comprehensive descriptions of...
Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis
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Simulation of Adaptive Running with Flexible Sports Prosthesis using Reinforcement Learning of Hybrid-link System
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What Do Students Learn? A Feature-Level Analysis of Dark Knowledge
arXiv:2606.03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations remain underexplored. In this work, we analyze student feature learning using the Interaction Tensor framework. Our analysis reveals that effective KD acts as a regularizer that prunes low-frequency, sample-specific features, encouraging the student to rely on a compact set of highly reusable...
Small-Signal Analyses Using Analytical IBR Models and Frequency-Dependent Th\'evenin Equivalents
arXiv:2606.04538v1 Announce Type: new Abstract: This paper investigates whether component-level studies can capture additional interactions through Small Signal Analysis (SSA) when the network connected to the Voltage Source Converter (VSC), typically modeled as a simple Thevenin Equivalent, is a more complex IBR-based network. The research investigates cases ranging from basic analytical to an IEEE 9-Bus EMT model, with and without Inverter-Based Resources (IBRs), synthesized as State-Space...
COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimation
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Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows
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Dynamic Interaction-Aware and Causality-Disentangled Framework for Multimodal Sentiment Analysis
arXiv:2605.30994v1 Announce Type: new Abstract: Although Multimodal Sentiment Analysis (MSA) effectively leverages rich information from language, visual, and acoustic modalities, existing methods still face two core challenges: 1) static conflict suppression mechanisms fail to adapt to dynamic variations across samples, and 2) the inherent sentimental bias within the language modality, which can misguide learning from other modalities, remains entangled. To this end, we propose a Dynamic...