A Meaningful Measure of Response Time for SLA. - Vikas Verma (Int. CMG Conference, 1992)

A Fair Basis For Charge Back. - Vikas Verma; Malcolm Thurgur (Int. CMG Conference, 1992)

QoS Based Pricing for Web Services. - George Eby Mathew; Joseph Shields; Vikas Verma (WISE Workshops, 2004)

Hand Gesture Reconstruction and Animation. - Vikas Verma; D. Ghosh (IICAI, 2005)

Image Retrieval And Classification Using Local Feature Vectors. - Vikas Verma (CoRR, 2014)

Residual Connections Encourage Iterative Inference. - Stanislaw Jastrzebski; Devansh Arpit; Nicolas Ballas; Vikas Verma; Tong Che; Yoshua Bengio (ICLR (Poster), 2018)

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. - Vikas Verma; Alex Lamb; Christopher Beckham; Aaron C. Courville; Ioannis Mitliagkas; Yoshua Bengio (CoRR, 2018)

Modularity Matters: Learning Invariant Relational Reasoning Tasks. - Jason Jo; Vikas Verma; Yoshua Bengio (CoRR, 2018)

Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy. - Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio (AISec@CCS, 2019)

Adversarial Mixup Resynthesizers. - Christopher Beckham; Sina Honari; Alex Lamb; Vikas Verma; Farnoosh Ghadiri; R. Devon Hjelm; Christopher J. Pal (DGS@ICLR, 2019)

Manifold Mixup: Better Representations by Interpolating Hidden States. - Vikas Verma; Alex Lamb; Christopher Beckham; Amir Najafi 002; Ioannis Mitliagkas; David Lopez-Paz; Yoshua Bengio (ICML, 2019)

Interpolation Consistency Training for Semi-supervised Learning. - Vikas Verma; Alex Lamb; Juho Kannala; Yoshua Bengio; David Lopez-Paz (IJCAI, 2019)

On Adversarial Mixup Resynthesis. - Christopher Beckham; Sina Honari; Vikas Verma; Alex Lamb; Farnoosh Ghadiri; R. Devon Hjelm; Yoshua Bengio; Chris Pal (NeurIPS, 2019)

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy. - Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio (CoRR, 2019)

Towards Understanding Generalization in Gradient-Based Meta-Learning. - Simon Guiroy; Vikas Verma; Christopher J. Pal (CoRR, 2019)

GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. - Vikas Verma; Meng Qu; Alex Lamb; Yoshua Bengio; Juho Kannala; Jian Tang 005 (CoRR, 2019)

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. - Fan-Yun Sun; Jordan Hoffmann; Vikas Verma; Jian Tang 005 (ICLR, 2020)

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. - Alex Lamb; Sherjil Ozair; Vikas Verma; David Ha (WACV, 2020)

PatchUp: A Regularization Technique for Convolutional Neural Networks. - Mojtaba Faramarzi; Mohammad Amini; Akilesh Badrinaaraayanan; Vikas Verma; Sarath Chandar (CoRR, 2020)

Towards Domain-Agnostic Contrastive Learning. - Vikas Verma; Minh-Thang Luong; Kenji Kawaguchi; Hieu Pham 001; Quoc V. Le (CoRR, 2020)

Algorithms for Data-Efficient Training of Deep Neural Networks. - Vikas Verma (2020)

GraphMix: Improved Training of GNNs for Semi-Supervised Learning. - Vikas Verma; Meng Qu; Kenji Kawaguchi; Alex Lamb; Yoshua Bengio; Juho Kannala; Jian Tang 005 (AAAI, 2021)

Interpolation-Based Semi-Supervised Learning for Object Detection. - Jisoo Jeong; Vikas Verma; Minsung Hyun; Juho Kannala; Nojun Kwak (CVPR, 2021)

Towards Domain-Agnostic Contrastive Learning. - Vikas Verma; Thang Luong; Kenji Kawaguchi; Hieu Pham 001; Quoc V. Le (ICML, 2021)

PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks. - Mojtaba Faramarzi; Mohammad Amini; Akilesh Badrinaaraayanan; Vikas Verma; Sarath Chandar (AAAI, 2022)

CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback. - Alexia Jolicoeur-Martineau; Alex Lamb; Vikas Verma; Aniket Didolkar (CoRR, 2022)

MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. - Vikas Verma; Sarthak Mittal; Wai Hoh Tang; Hieu Pham 001; Juho Kannala; Yoshua Bengio; Arno Solin; Kenji Kawaguchi (CoRR, 2022)

Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy. - Alex Lamb; Vikas Verma; Kenji Kawaguchi; Alexander Matyasko; Savya Khosla; Juho Kannala; Yoshua Bengio (Neural Networks, 2022)

Interpolation consistency training for semi-supervised learning. - Vikas Verma; Kenji Kawaguchi; Alex Lamb; Juho Kannala; Arno Solin; Yoshua Bengio; David Lopez-Paz (Neural Networks, 2022)

Critical Analysis of Existing Punjabi Grammar Checker and a Proposed Hybrid Framework Involving Machine Learning and Rule-Base Criteria. - Vikas Verma; S. K. Sharma (ACM Trans. Asian Low Resour. Lang. Inf. Process., 2022)

MixupE: Understanding and improving Mixup from directional derivative perspective. - Yingtian Zou; Vikas Verma; Sarthak Mittal; Wai Hoh Tang; Hieu Pham 001; Juho Kannala; Yoshua Bengio; Arno Solin; Kenji Kawaguchi (UAI, 2023)

Leveraging Out-of-Domain Data for Domain-Specific Prompt Tuning in Multi-Modal Fake News Detection. - Debarshi Brahma; Amartya Bhattacharya; Suraj Nagaje Mahadev; Anmol Asati; Vikas Verma; Soma Biswas (CoRR, 2023)

Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach. - Vikas Verma; Arun Malik; Isha Batra (Int. J. Perform. Eng., 2024)

Can Out-of-Domain Data Help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection? - Amartya Bhattacharya; Debarshi Brahma; Suraj Nagaje Mahadev; Anmol Asati; Vikas Verma; Soma Biswas (WACV, 2025)

FRAUD-Net: Fraud News Detection Using Sample Uncertainty & Domain Aware Generalized Network. - Devendra Patel; Vikas Verma; Shreyas Kumar Tah; Shwetabh Biswas; Soma Biswas (WACV, 2025)

Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2. - Yuri Chervonyi; Trieu H. Trinh; Miroslav Olsák; Xiaomeng Yang; Hoang Nguyen; Marcelo Menegali; Junehyuk Jung; Vikas Verma; Quoc V. Le; Thang Luong (CoRR, 2025)