Search Results for author: Ankit B. Patel

Found 21 papers, 5 papers with code

A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks

no code implementations14 Jul 2023 Ryan Pyle, Sebastian Musslick, Jonathan D. Cohen, Ankit B. Patel

A key property of neural networks (both biological and artificial) is how they learn to represent and manipulate input information in order to solve a task.

Linking convolutional kernel size to generalization bias in face analysis CNNs

no code implementations7 Feb 2023 Hao Liang, Josue Ortega Caro, Vikram Maheshri, Ankit B. Patel, Guha Balakrishnan

Our framework is experimental, in that we train several versions of a network with an intervention to a specific hyperparameter, and measure the resulting causal effect of this choice on performance bias when a particular out-of-distribution image perturbation is applied.

Dyadic Interaction Assessment from Free-living Audio for Depression Severity Assessment

no code implementations8 Sep 2022 Bishal Lamichhane, Nidal Moukaddam, Ankit B. Patel, Ashutosh Sabharwal

Psychomotor retardation in depression has been associated with speech timing changes from dyadic clinical interviews.


Understanding robustness and generalization of artificial neural networks through Fourier masks

1 code implementation16 Mar 2022 Nikos Karantzas, Emma Besier, Josue Ortega Caro, Xaq Pitkow, Andreas S. Tolias, Ankit B. Patel, Fabio Anselmi

Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place.

Data Augmentation

An Improved Semi-Supervised VAE for Learning Disentangled Representations

no code implementations12 Jun 2020 Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk

Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning.


Adversarial Attacks on Machine Learning Systems for High-Frequency Trading

no code implementations21 Feb 2020 Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein

Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain.

Algorithmic Trading BIG-bench Machine Learning +1

DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems

no code implementations25 Sep 2019 Varun Suriyanarayana, Onur Tavaslioglu, Ankit B. Patel, Andrew J. Schaefer

We use deep value-based reinforcement learning to learn a pivoting strategy that at each iteration chooses between two of the most popular pivot rules -- Dantzig and steepest edge.

Combinatorial Optimization Traveling Salesman Problem

Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs

no code implementations ICLR 2019 Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Swarat Chaudhuri, Ankit B. Patel

We study the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.


Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

no code implementations ICLR 2019 Nhat Ho, Tan Nguyen, Ankit B. Patel, Anima Anandkumar, Michael. I. Jordan, Richard G. Baraniuk

The conjugate prior yields a new regularizer for learning based on the paths rendered in the generative model for training CNNs–the Rendering Path Normalization (RPN).

Neural Rendering

Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks

no code implementations27 Feb 2019 Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel

We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.


A Probabilistic Framework for Deep Learning

no code implementations NeurIPS 2016 Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables.

General Classification

Semi-Supervised Learning with the Deep Rendering Mixture Model

no code implementations6 Dec 2016 Tan Nguyen, Wanjia Liu, Ethan Perez, Richard G. Baraniuk, Ankit B. Patel

Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning.

Variational Inference

A Deep Learning Approach to Structured Signal Recovery

no code implementations17 Aug 2015 Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk

In this paper, we develop a new framework for sensing and recovering structured signals.

Compressive Sensing Denoising

A Probabilistic Theory of Deep Learning

1 code implementation2 Apr 2015 Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation.

Object Object Recognition +2

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