250 citations · 532 across the 72 of their papers we have counts for
11 papers · 1 filter
Stock Market Prediction via Deep Learning Techniques: A Survey
Jinan Zou, Qingying Zhao, Yang Jiao +6
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comp…
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi +1
We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network…
LAVA: Label-efficient Visual Learning and Adaptation
Islam Nassar, Munawar Hayat, Ehsan Abbasnejad +3
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data. LAVA builds on a few recent innovations to enable adapting to partially…
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
Damien Teney, Yong Lin, Seong Joon Oh +1
Several studies have compared the in-distribution (ID) and out-of-distribution (OOD) performance of models in computer vision and NLP. They report a frequent positive correlation a…
Truncated Matrix Power Iteration for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +5
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization prob…
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
Damien Teney, Maxime Peyrard, Ehsan Abbasnejad
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model,…