41 citations · 81 across the 21 of their papers we have counts for
22 papers
Latent-Kernel Discrete Flow Maps for Few-Step Generation
Mansoor Ahmed, Yue-Tsz Fan, Hemanth Venkateswara +1
Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token i…
EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning
Mansoor Ahmed, Huirong Chai, Haoxin Wang +2
Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes. Computational epitope prediction is critical for understanding immune recognition and…
CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design
Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan +2
Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized be…
Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
Adele Chinda, Richmond Azumah, Hemanth Demakethepalli Venkateswara
Wastewater-based genomic surveillance has emerged as a powerful tool for population-level viral monitoring, offering comprehensive insights into circulating viral variants across e…
Label Smoothing++: Enhanced Label Regularization for Training Neural Networks
Sachin Chhabra, Hemanth Venkateswara, Baoxin Li
Training neural networks with one-hot target labels often results in overconfidence and overfitting. Label smoothing addresses this issue by perturbing the one-hot target labels by…
Dataset Augmentation by Mixing Visual Concepts
Abdullah Al Rahat, Hemanth Venkateswara
This paper proposes a dataset augmentation method by fine-tuning pre-trained diffusion models. Generating images using a pre-trained diffusion model with textual conditioning often…
Domain Adaptation Using Pseudo Labels
Sachin Chhabra, Hemanth Venkateswara, Baoxin Li
In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a class…
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation
Sandipan Choudhuri, Suli Adeniye, Arunabha Sen +1
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. Howe…
PatchRot: A Self-Supervised Technique for Training Vision Transformers
Sachin Chhabra, Prabal Bijoy Dutta, Hemanth Venkateswara +1
Vision transformers require a huge amount of labeled data to outperform convolutional neural networks. However, labeling a huge dataset is a very expensive process. Self-supervised…
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
Sandipan Choudhuri, Hemanth Venkateswara, Arunabha Sen
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact…
Sparsity Regularization For Cold-Start Recommendation
Aksheshkumar Ajaykumar Shah, Hemanth Venkateswara
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extr…
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation
Sandipan Choudhuri, Riti Paul, Arunabha Sen +2
The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most real-world sce…
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
Maunil R Vyas, Hemanth Venkateswara, Sethuraman Panchanathan
Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Generative mod…
Foveated Haptic Gaze
Bijan Fakhri, Troy McDaniel, Heni Ben Amor +3
As digital worlds become ubiquitous via video games, simulations, virtual and augmented reality, people with disabilities who cannot access those worlds are becoming increasingly d…
Representation, Exploration and Recommendation of Music Playlists
Piyush Papreja, Hemanth Venkateswara, Sethuraman Panchanathan
Playlists have become a significant part of our listening experience because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise i…
Efficient Approximate Solutions to Mutual Information Based Global Feature Selection
Hemanth Venkateswara, Prasanth Lade, Binbin Lin +2
Mutual Information (MI) is often used for feature selection when developing classifier models. Estimating the MI for a subset of features is often intractable. We demonstrate, that…
Multiresolution Match Kernels for Gesture Video Classification
Hemanth Venkateswara, Vineeth N. Balasubramanian, Prasanth Lade +1
The emergence of depth imaging technologies like the Microsoft Kinect has renewed interest in computational methods for gesture classification based on videos. For several years no…
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel +1
Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce th…
Coupled Support Vector Machines for Supervised Domain Adaptation
Hemanth Venkateswara, Prasanth Lade, Jieping Ye +1
Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We presen…
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlin…
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty +1
In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a…
A Strategy for an Uncompromising Incremental Learner
Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan +1
Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting.…