270 citations · 1.1k across the 72 of their papers we have counts for
12 papers · 1 filter
A Maximal Correlation Approach to Imposing Fairness in Machine Learning
Joshua Lee, Yuheng Bu, Prasanna Sattigeri +4
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explor…
Optimizing Mode Connectivity via Neuron Alignment
N. Joseph Tatro, Pin-Yu Chen, Payel Das +3
The loss landscapes of deep neural networks are not well understood due to their high nonconvexity. Empirically, the local minima of these loss functions can be connected by a lear…
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang +12
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
Seungwook Han, Akash Srivastava, Cole Hurwitz +2
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train,…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky +3
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies…
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda +5
Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense lim…
Causal Feature Selection for Algorithmic Fairness
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri +1
The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the…
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan +1
The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous…
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris +2
The field of Few-Shot Learning (FSL), or learning from very few (typically or ) examples per novel class (unseen during training), has received a lot of attention and signif…
StarNet: towards Weakly Supervised Few-Shot Object Detection
Leonid Karlinsky, Joseph Shtok, Amit Alfassy +8
Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and fo…
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli +1
The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generaliza…