264 citations · 652 across the 29 of their papers we have counts for
12 papers · 1 filter
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
There is extensive interest in metric learning methods for image retrieval. Many metric learning loss functions focus on learning a correct ranking of training samples, but strongl…
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution l…
Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting
Mohammad Omar Khursheed, Christin Jose, Rajath Kumar +3
In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural Network) models applied to the problem of wakeword detection, and augment them with scaled dot product atten…
-Annealed Variational Autoencoder for glitches
Sivaramakrishnan Sankarapandian, Brian Kulis
Gravitational wave detectors such as LIGO and Virgo are susceptible to various types of instrumental and environmental disturbances known as glitches which can mask and mimic gravi…
Deep Divergence Learning
Kubra Cilingir, Rachel Manzelli, Brian Kulis
Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural network…
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Xiao Wang, Siyue Wang, Pin-Yu Chen +4
Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on…