iSparse: Output Informed Sparsification of Neural Networks

no code implementations • 25 Sep 2019 • Yash Garg, K. Selcuk Candan

We note that many successful networks nevertheless often contain large numbers of redundant edges.

Management

Evaluation Methods and Measures for Causal Learning Algorithms

no code implementations • 7 Feb 2022 • Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K. Selcuk Candan, Huan Liu

To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.

Benchmarking BIG-bench Machine Learning +1

Class-Specific Attention (CSA) for Time-Series Classification

no code implementations • 19 Nov 2022 • Yifan Hao, Huiping Cao, K. Selcuk Candan, Jiefei Liu, Huiying Chen, Ziwei Ma

In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series.

Classification Time Series +2

UPREVE: An End-to-End Causal Discovery Benchmarking System

no code implementations • 25 Jul 2023 • Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, K. Selcuk Candan

Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making.

Benchmarking Causal Discovery +1