Search Results for author: Abhishek Kumar

Found 62 papers, 16 papers with code

On Implicit Regularization in $\beta$-VAEs

no code implementations ICML 2020 Abhishek Kumar, Ben Poole

While the impact of variational inference (VI) on posterior inference in a fixed generative model is well-characterized, its role in regularizing a learned generative model when used in variational autoencoders (VAEs) is poorly understood.

Variational Inference

To Aggregate or Not? Learning with Separate Noisy Labels

no code implementations14 Jun 2022 Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu

The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).

Learning with noisy labels

GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking

no code implementations CVPR 2022 Abhishek Kumar, Oladayo S. Ajani, Swagatam Das, Rammohan Mallipeddi

To address this issue, we propose a mode-seeking algorithm called GridShift, with significant speedup and principally based on MS. To accelerate, GridShift employs a grid-based approach for neighbor search, which is linear in the number of data points.

Object Tracking Semantic Segmentation

DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents

1 code implementation2 Jan 2022 Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar

Furthermore, we show that the proposed model can generate high-resolution samples and exhibits synthesis quality comparable to state-of-the-art models on standard benchmarks.

Denoising Image Generation +1

Solving Inverse Problems with NerfGANs

no code implementations16 Dec 2021 Giannis Daras, Wen-Sheng Chu, Abhishek Kumar, Dmitry Lagun, Alexandros G. Dimakis

We introduce a novel framework for solving inverse problems using NeRF-style generative models.

When Creators Meet the Metaverse: A Survey on Computational Arts

no code implementations26 Nov 2021 Lik-Hang Lee, Zijun Lin, Rui Hu, Zhengya Gong, Abhishek Kumar, Tangyao Li, Sijia Li, Pan Hui

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity.

Constrained Instance and Class Reweighting for Robust Learning under Label Noise

no code implementations9 Nov 2021 Abhishek Kumar, Ehsan Amid

However, their performance is largely dependent on the quality of the training data and often degrades in the presence of noise.

Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications

no code implementations25 Oct 2021 Young D. Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui, Cecilia Mascolo

Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%).

Continual Learning Incremental Learning +1

Implicit Rate-Constrained Optimization of Non-decomposable Objectives

2 code implementations23 Jul 2021 Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter

We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholded form, while constraining another metric of interest.

Wavelet Design in a Learning Framework

no code implementations23 Jul 2021 Dhruv Jawali, Abhishek Kumar, Chandra Sekhar Seelamantula

Wavelets have proven to be highly successful in several signal and image processing applications.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval

1 code implementation ICCV 2021 Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar, David Forsyth

We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images.

Disentanglement

Physics-Guided Deep Neural Network to Characterize Non-Newtonian Fluid Flow for Optimal Use of Energy Resources

no code implementations Expert Systems with Applications 2021 Abhishek Kumar, Syahrir Ridha, Narahari Marneni, Suhaib Umer Ilyas

The uncertainty in fluid consistency index is responsible for higher variance in the calculated flow rate, while the least variation is observed due to fluid behavior index uncertainty.

Risk score learning for COVID-19 contact tracing apps

1 code implementation17 Apr 2021 Kevin Murphy, Abhishek Kumar, Stylianos Serghiou

Although this data is already being collected (in an aggregated, privacy-preserving way) by several health authorities, in this paper we limit ourselves to simulated data, so that we can systematically study the different factors that affect the feasibility of the approach.

Privacy Preserving

A Unified Bayesian Framework for Discriminative and Generative Continual Learning

no code implementations1 Jan 2021 Abhishek Kumar, Sunabha Chatterjee, Piyush Rai

Two notable directions among the recent advances in continual learning with neural networks are (1) variational Bayes based regularization by learning priors from previous tasks, and, (2) learning the structure of deep networks to adapt to new tasks.

Continual Learning

Floquet Gauge Pumps as Sensors for Spectral Degeneracies Protected by Symmetry or Topology

no code implementations17 Dec 2020 Abhishek Kumar, Gerardo Ortiz, Philip Richerme, Babak Seradjeh

The dynamically generated magnetization current depends on the phases of complex coupling terms, with the XY interaction as the real and DMI as the imaginary part.

Quantum Gases Mesoscale and Nanoscale Physics Superconductivity

A thermodynamic probe of the topological phase transition in epitaxial graphene based Floquet topological insulator

no code implementations3 Dec 2020 Abhishek Kumar, Colin Benjamin

In this paper, we probe the topological phase transition of an FTI via the efficiency and work output of quantum Otto and quantum Stirling heat engines.

Mesoscale and Nanoscale Physics Applied Physics Quantum Physics

Score-Based Generative Modeling through Stochastic Differential Equations

8 code implementations ICLR 2021 Yang song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.

Colorization Image Inpainting +1

Adaptive Contention Window Design using Deep Q-learning

1 code implementation18 Nov 2020 Abhishek Kumar, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of adaptive contention window (CW) design for random-access wireless networks.

Q-Learning

Few-Shot Adaptation of Generative Adversarial Networks

1 code implementation22 Oct 2020 Esther Robb, Wen-Sheng Chu, Abhishek Kumar, Jia-Bin Huang

We validate our method in a challenging few-shot setting of 5-100 images in the target domain.

Image Generation

Generalized Adversarially Learned Inference

no code implementations15 Jun 2020 Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai

Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs.

On the Inhibition of COVID-19 Protease by Indian Herbal Plants: An In Silico Investigation

no code implementations5 Apr 2020 Ambrish Kumar Srivastava, Abhishek Kumar, Neeraj Misra

This study aims to assess the Indian herbal plants in the pursuit of potential COVID-19 inhibitors using in silico approaches.

Marketplace for AI Models

no code implementations3 Mar 2020 Abhishek Kumar, Benjamin Finley, Tristan Braud, Sasu Tarkoma, Pan Hui

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation.

Regularized Autoencoders via Relaxed Injective Probability Flow

no code implementations20 Feb 2020 Abhishek Kumar, Ben Poole, Kevin Murphy

Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference.

On Implicit Regularization in $β$-VAEs

no code implementations31 Jan 2020 Abhishek Kumar, Ben Poole

While the impact of variational inference (VI) on posterior inference in a fixed generative model is well-characterized, its role in regularizing a learned generative model when used in variational autoencoders (VAEs) is poorly understood.

Variational Inference

Bayesian Structure Adaptation for Continual Learning

1 code implementation8 Dec 2019 Abhishek Kumar, Sunabha Chatterjee, Piyush Rai

Two notable directions among the recent advances in continual learning with neural networks are ($i$) variational Bayes based regularization by learning priors from previous tasks, and, ($ii$) learning the structure of deep networks to adapt to new tasks.

Continual Learning

Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

no code implementations28 Nov 2019 Abhishek Kumar, Asif Ekbal, Daisuke Kawahra, Sadao Kurohashi

Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.

Emotion Recognition Sentiment Analysis

Customized video filtering on YouTube

no code implementations11 Nov 2019 Vishal Anand, Ravi Shukla, Ashwani Gupta, Abhishek Kumar

But with a huge surge in content being posted online it becomes seemingly difficult to filter out related videos on which they can run their ads without compromising brand name.

Weakly Supervised Disentanglement with Guarantees

1 code implementation ICLR 2020 Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.

Disentanglement

Refined $α$-Divergence Variational Inference via Rejection Sampling

no code implementations17 Sep 2019 Rahul Sharma, Abhishek Kumar, Piyush Rai

Our inference method is based on a crucial observation that $D_\infty(p||q)$ equals $\log M(\theta)$ where $M(\theta)$ is the optimal value of the RS constant for a given proposal $q_\theta(x)$.

Variational Inference

Multilingual and Multitarget Hate Speech Detection in Tweets

no code implementations JEPTALNRECITAL 2019 Patricia Chiril, Farah Benamara Zitoune, V{\'e}ronique Moriceau, Marl{\`e}ne Coulomb-Gully, Abhishek Kumar

Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated.

Feature Engineering Hate Speech Detection

The binary trio at SemEval-2019 Task 5: Multitarget Hate Speech Detection in Tweets

no code implementations SEMEVAL 2019 Patricia Chiril, Farah Benamara Zitoune, V{\'e}ronique Moriceau, Abhishek Kumar

The massive growth of user-generated web content through blogs, online forums and most notably, social media networks, led to a large spreading of hatred or abusive messages which have to be moderated.

Feature Engineering Hate Speech Detection

A Scale Invariant Flatness Measure for Deep Network Minima

no code implementations6 Feb 2019 Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar, Dzung Phan, Sang H. Chin, Trac. D. Tran

It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization.

Understanding Unequal Gender Classification Accuracy from Face Images

no code implementations30 Nov 2018 Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.

Classification General Classification

SpotTune: Transfer Learning through Adaptive Fine-tuning

3 code implementations CVPR 2019 Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris

Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision.

Inductive Bias Transfer Learning

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

1 code implementation14 Nov 2018 Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar

Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.

Band Gap Formation Energy +1

Co-regularized Alignment for Unsupervised Domain Adaptation

no code implementations NeurIPS 2018 Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}.

Unsupervised Domain Adaptation

Improved Neural Text Attribute Transfer with Non-parallel Data

no code implementations26 Nov 2017 Igor Melnyk, Cicero Nogueira dos santos, Kahini Wadhawan, Inkit Padhi, Abhishek Kumar

Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.

Disentanglement Text Attribute Transfer

BlockDrop: Dynamic Inference Paths in Residual Networks

1 code implementation CVPR 2018 Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris

Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications.

The Riemannian Geometry of Deep Generative Models

no code implementations21 Nov 2017 Hang Shao, Abhishek Kumar, P. Thomas Fletcher

Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space.

Translation

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

1 code implementation ICLR 2018 Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.

Disentanglement Variational Inference

A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis

no code implementations EMNLP 2017 Md. Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya

In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis.

Sentiment Analysis Stock Prediction +1

SenGen: Sentence Generating Neural Variational Topic Model

no code implementations1 Aug 2017 Ramesh Nallapati, Igor Melnyk, Abhishek Kumar, Bo-Wen Zhou

We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence.

Local Group Invariant Representations via Orbit Embeddings

no code implementations6 Dec 2016 Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf

We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations.

Rotated MNIST

S3Pool: Pooling with Stochastic Spatial Sampling

4 code implementations CVPR 2017 Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogerio Feris

We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.

Data Augmentation Image Classification

A new Initial Centroid finding Method based on Dissimilarity Tree for K-means Algorithm

no code implementations19 Jun 2015 Abhishek Kumar, Suresh Chandra Gupta

Cluster analysis is one of the primary data analysis technique in data mining and K-means is one of the commonly used partitioning clustering algorithm.

Exact and Heuristic Algorithms for Semi-Nonnegative Matrix Factorization

no code implementations27 Oct 2014 Nicolas Gillis, Abhishek Kumar

Second, we propose an exact algorithm (that is, an algorithm that finds an optimal solution), also based on the SVD, for a certain class of matrices (including nonnegative irreducible matrices) from which we derive an initialization for matrices not belonging to that class.

Near-separable Non-negative Matrix Factorization with $\ell_1$- and Bregman Loss Functions

no code implementations27 Dec 2013 Abhishek Kumar, Vikas Sindhwani

Recently, a family of tractable NMF algorithms have been proposed under the assumption that the data matrix satisfies a separability condition Donoho & Stodden (2003); Arora et al. (2012).

Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression

no code implementations NeurIPS 2012 Piyush Rai, Abhishek Kumar, Hal Daume

In this paper, we present a multiple-output regression model that leverages the covariance structure of the functions (i. e., how the multiple functions are related with each other) as well as the conditional covariance structure of the outputs.

Learning Task Grouping and Overlap in Multi-task Learning

no code implementations27 Jun 2012 Abhishek Kumar, Hal Daume III

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks.

Multi-Task Learning

Co-regularized Multi-view Spectral Clustering

no code implementations NeurIPS 2011 Abhishek Kumar, Piyush Rai, Hal Daume

In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering.

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