26 citations · 37 across the 12 of their papers we have counts for
14 papers
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela +3
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework s…
AptSim2Real: Approximately-Paired Sim-to-Real Image Translation
Charles Y Zhang, Ashish Shrivastava
Advancements in graphics technology has increased the use of simulated data for training machine learning models. However, the simulated data often differs from real-world data, cr…
I see what you hear: a vision-inspired method to localize words
Mohammad Samragh, Arnav Kundu, Ting-Yao Hu +5
This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contempora…
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
Jen-Hao Rick Chang, Ashish Shrivastava, Hema Swetha Koppula +2
Controllable generative sequence models with the capability to extract and replicate the style of specific examples enable many applications, including narrating audiobooks in diff…
CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems
Aadesh Gupta, Kaustubh D. Dhole, Rahul Tarway +2
Domain-specific dialogue systems generally determine user intents by relying on sentence level classifiers that mainly focus on single action sentences. Such classifiers are not de…
SLAP: A Split Latency Adaptive VLIW pipeline architecture which enables on-the-fly variable SIMD vector-length
Ashish Shrivastava, Alan Gatherer, Tong Sun +2
Over the last decade the relative latency of access to shared memory by multicore increased as wire resistance dominated latency and low wire density layout pushed multiport memori…
Towards a Domain Specific Solution for a New Generation of Wireless Modems
Alan Gatherer, Ashish Shrivastava, Hao Luan +3
Wireless cellular System on Chip (SoC) are experiencing unprecedented demands on data rate, latency use case variety. 5G wireless technologies require a massive number of antennas…
Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
Ashish Shrivastava, Kaustubh Dhole, Abhinav Bhatt +1
Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid…
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric
Ashish Shrivastava, Arnav Kundu, Chandra Dhir +2
Deep Neural Network--Hidden Markov Model (DNN-HMM) based methods have been successfully used for many always-on keyword spotting algorithms that detect a wake word to trigger a dev…
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
Ting-Yao Hu, Ashish Shrivastava, Oncel Tuzel +1
We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, suc…
Divide, Denoise, and Defend against Adversarial Attacks
Seyed-Mohsen Moosavi-Dezfooli, Ashish Shrivastava, Oncel Tuzel
Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustn…
Learning from Simulated and Unsupervised Images through Adversarial Training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel +3
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from…