Search Results for author:
Found 15 papers, 7 papers with code
Educator Attention: How computational tools can systematically identify the distribution of a key resource for students
Educator attention is critical for student success, yet how educators distribute their attention across students remains poorly understood due to data and methodological constraints.
COME: Test-time adaption by Conservatively Minimizing Entropy
Machine learning models must continuously self-adjust themselves for novel data distribution in the open world.
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection
To our best knowledge, this work is the first principled OOD detection method that achieves state-of-the-art OOD detection performance without compromising OOD generalization ability.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
We first use GPT-4 to create a small labeled dataset, then fine-tune a Llama3-8B model on it.
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis.
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
Many complicated real-world tasks can be broken down into smaller, more manageable parts, and planning with prior knowledge extracted from these simplified pieces is crucial for humans to make accurate decisions.
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes
We evaluate state-of-the-art LLMs on our dataset and find that the expert's decision-making model is critical for LLMs to close the gap: responses from GPT4 with expert decisions (e. g., "simplify the problem") are +76% more preferred than without.
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
However, existing works often overlook the temporal coherence in GCHRL when learning latent subgoal representations and lack an efficient subgoal selection strategy that balances exploration and exploitation.
Proceedings of the 40th International Conference on Machine Learning
Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted.
Provable Dynamic Fusion for Low-Quality Multimodal Data
The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction.
Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation
Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments.
X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages
(3) Integrating multiple modalities: all single-modal encoders are aligned with the LLM through X2L interfaces to integrate multimodal capabilities into the LLM.
Given Users Recommendations Based on Reviews on Yelp
For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert model and word2vec model; the second part is the implementation of an item-based collaborative filtering algorithm to compute the similarity of each review under different categories of restaurants.
Action Unit Detection with Joint Adaptive Attention and Graph Relation
This paper describes an approach to the facial action unit (AU) detection.
Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Distributed, Parallel, and Cluster Computing Robotics
