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12/12/2022
Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection
In object detection, post-processing methods like Non-maximum Suppressio...
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09/30/2021
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
We develop biologically plausible training mechanisms for self-supervise...
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05/19/2021
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Unsupervised outlier detection, which predicts if a test sample is an ou...
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02/23/2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Maximum likelihood estimation is widely used in training Energy-based mo...
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06/15/2020
Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy
In this paper, we present a general method that can improve the sample q...
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03/06/2020
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Deep probabilistic generative models enable modeling the likelihoods of ...
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11/05/2019
A Method to Model Conditional Distributions with Normalizing Flows
In this work, we investigate the use of normalizing flows to model condi...
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05/24/2019
Generative Latent Flow: A Framework for Non-adversarial Image Generation
Generative Adversarial Networks (GANs) have been shown to outperform non...
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11/19/2018
Biologically plausible deep learning
Building on the model proposed in Lillicrap et. al. we show that deep ne...
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12/18/2017
Deformable Classifiers
Geometric variations of objects, which do not modify the object class, p...
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02/16/2017
Dynamic Partition Models
We present a new approach for learning compact and intuitive distributed...
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11/15/2015
Mixtures of Sparse Autoregressive Networks
We consider high-dimensional distribution estimation through autoregress...
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12/11/2014