74 citations · 115 across the 13 of their papers we have counts for
13 papers
Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection
Angzhi Fan, Benjamin Ticknor, Yali Amit
In object detection, post-processing methods like Non-maximum Suppression (NMS) are widely used. NMS can substantially reduce the number of false positive detections but may still…
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
Mufeng Tang, Yibo Yang, Yali Amit
We develop biologically plausible training mechanisms for self-supervised learning (SSL) in deep networks. Specifically, by biological plausible training we mean (i) All updates of…
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Zhisheng Xiao, Qing Yan, Yali Amit
Unsupervised outlier detection, which predicts if a test sample is an outlier or not using only the information from unlabelled inlier data, is an important but challenging task. R…
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Zhisheng Xiao, Qing Yan, Yali Amit
Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and…
Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy
Zhisheng Xiao, Qing Yan, Yali Amit
In this paper, we present a general method that can improve the sample quality of pre-trained likelihood based generative models. Our method constructs an energy function on the la…
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Zhisheng Xiao, Qing Yan, Yali Amit
Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect…