Search Results for author:
Found 14 papers, 3 papers with code
CausalGraph2LLM: Evaluating LLMs for Causal Queries
In this paper, we propose a comprehensive benchmark, \emph{CausalGraph2LLM}, encompassing a variety of causal graph settings to assess the causal graph understanding capability of LLMs.
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data.
Hypothesizing Missing Causal Variables with LLMs
Motivated by the scientific discovery process, in this work, we formulate a novel task where the input is a partial causal graph with missing variables, and the output is a hypothesis about the missing variables to complete the partial graph.
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications.
Transparent Anomaly Detection via Concept-based Explanations
The task of anomaly detection (AD) focuses on finding whether a given sample follows the learned distribution.
WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada.
Source-free Domain Adaptation Requires Penalized Diversity
In addition, PD is augmented with a weighted MI maximization objective for label distribution shift.
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates.
Learning Latent Structural Causal Models
For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.
WiCV 2022: The Tenth Women In Computer Vision Workshop
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana.
Latent Variable Models for Bayesian Causal Discovery
Learning predictors that do not rely on spurious correlations involves building causal representations.
FHIST: A Benchmark for Few-shot Classification of Histological Images
We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.
Three-stream network for enriched Action Recognition
Understanding accurate information on human behaviours is one of the most important tasks in machine intelligence.
