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Found 12 papers, 5 papers with code
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details.
Large language models can enhance persuasion through linguistic feature alignment
Based on the results of these observational studies, we hypothesize that LLM usage may enhance a comprehensive set of linguistic features, increasing message persuasiveness to receivers with heterogeneous linguistic preferences (i. e., linguistic feature alignment).
Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment.
SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
Specifically, we use a Low-Rank Adaptive Method (LoRA) technique, limiting each expert size as only 0. 06% of number of StarCoder's parameters.
Knowing Where to Focus: Event-aware Transformer for Video Grounding
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries.
ChatGPT Is More Likely to Be Perceived as Male Than Female
We investigate how people perceive ChatGPT, and, in particular, how they assign human-like attributes such as gender to the chatbot.
Learning to Detect Touches on Cluttered Tables
We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables.
Probabilistic Prompt Learning for Dense Prediction
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models.
Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty
We then examine human players' strategies across time and find that novel decisions (i. e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI.
Pin the Memory: Learning to Generalize Semantic Segmentation
The rise of deep neural networks has led to several breakthroughs for semantic segmentation.
Self-balanced Learning For Domain Generalization
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics.
Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo
Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision making abilities.
Decision Making
Human-Computer Interaction
General Economics
Economics
Applications
