Search Results for author:
Found 10 papers, 3 papers with code
Rotationally-Temporally Consistent Novel View Synthesis of Human Performance Video
Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding
Recent advancements in language-model-based video understanding have been progressing at a remarkable pace, spurred by the introduction of Large Language Models (LLMs).
Fairness-Aware Domain Generalization under Covariate and Dependence Shifts
Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.
Measuring and Modeling Physical Intrinsic Motivation
Finally, linearly combining the adversarial model with the number of collisions in a scene leads to the greatest improvement in predictivity of human responses, suggesting humans are driven towards scenarios that result in high information gain and physical activity.
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Automatic Forecasting via Meta-Learning
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
Learning to communicate about shared procedural abstractions
Many real-world tasks require agents to coordinate their behavior to achieve shared goals.
