1 code implementation • EMNLP (sustainlp) 2021 • Ankit Gupta, Guy Dar, Shaya Goodman, David Ciprut, Jonathan Berant
Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.
no code implementations • 31 Jan 2024 • Ankit Gupta, George Saon, Brian Kingsbury
The emergence of industrial-scale speech recognition (ASR) models such as Whisper and USM, trained on 1M hours of weakly labelled and 12M hours of audio only proprietary data respectively, has led to a stronger need for large scale public ASR corpora and competitive open source pipelines.
1 code implementation • 4 Oct 2023 • Ido Amos, Jonathan Berant, Ankit Gupta
Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences.
no code implementations • 4 Sep 2023 • Manas Mejari, Ankit Gupta, Dario Piga
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances.
1 code implementation • 14 Jul 2023 • Yuji Hirono, Ankit Gupta, Mustafa Khammash
The present results indicate that an RPA property is essentially equivalent to the existence of a "topological invariant", which is an instance of what we call the "Robust Adaptation is Topological"(RAT) principle.
1 code implementation • 26 Jun 2023 • Ankit Gupta, Ida-Maria Sintorn
A vector quantization step is introduced before the NN calculation to represent the template with $k$ features, and the filter response over the NNFs is used to compare the template and query distributions over the features.
no code implementations • 31 Mar 2023 • Manas Mejari, Ankit Gupta
This paper presents a direct data-driven approach for computing robust control invariant (RCI) sets and their associated state-feedback control laws for linear time-invariant systems affected by bounded disturbances.
no code implementations • 27 Feb 2023 • George Saon, Ankit Gupta, Xiaodong Cui
We improve on the popular conformer architecture by replacing the depthwise temporal convolutions with diagonal state space (DSS) models.
1 code implementation • 1 Dec 2022 • Ankit Gupta, Harsh Mehta, Jonathan Berant
Sequence models based on linear state spaces (SSMs) have recently emerged as a promising choice of architecture for modeling long range dependencies across various modalities.
no code implementations • 24 Oct 2022 • Abhijeet Bishnu, Ankit Gupta, Mandar Gogate, Kia Dashtipour, Ahsan Adeel, Amir Hussain, Mathini Sellathurai, Tharmalingam Ratnarajah
In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology.
no code implementations • 20 Oct 2022 • Ankit Gupta, Ida-Maria Sintorn
In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance.
1 code implementation • 6 Sep 2022 • Guy Dar, Mor Geva, Ankit Gupta, Jonathan Berant
In this work, we present a theoretical analysis where all parameters of a trained Transformer are interpreted by projecting them into the embedding space, that is, the space of vocabulary items they operate on.
1 code implementation • 27 Jun 2022 • Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur
State space models have shown to be effective at modeling long range dependencies, specially on sequence classification tasks.
2 code implementations • 23 Jun 2022 • Albert Gu, Ankit Gupta, Karan Goel, Christopher Ré
On the other hand, a recent variant of S4 called DSS showed that restricting the state matrix to be fully diagonal can still preserve the performance of the original model when using a specific initialization based on approximating S4's matrix.
Ranked #9 on Long-range modeling on LRA
2 code implementations • 27 Mar 2022 • Ankit Gupta, Albert Gu, Jonathan Berant
Modeling long range dependencies in sequential data is a fundamental step towards attaining human-level performance in many modalities such as text, vision, audio and video.
Ranked #11 on Long-range modeling on LRA
2 code implementations • 10 Jan 2022 • Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.
Ranked #8 on Long-range modeling on SCROLLS
1 code implementation • 14 Oct 2021 • Zhou Fang, Ankit Gupta, Mustafa Khammash
In this case, the regularized particle filter (RPF) is preferred to the conventional ones, as the RPF can mitigate sample degeneracy by perturbing particles with artificial noise.
1 code implementation • 13 Jun 2021 • Ankit Gupta, Guy Dar, Shaya Goodman, David Ciprut, Jonathan Berant
Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.
1 code implementation • 6 Jun 2021 • Zhou Fang, Ankit Gupta, Mustafa Khammash
As a result, we are able to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model.
1 code implementation • EMNLP 2021 • Ankit Gupta, Jonathan Berant
Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length.
no code implementations • 21 Sep 2020 • Ankit Gupta, Manas Mejari, Paolo Falcone, Dario Piga
This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems.
2 code implementations • NAACL 2021 • Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
1 code implementation • 5 Jun 2020 • Ankit Gupta, Jonathan Berant
Moreover, global memory can also be used for sequence compression, by representing a long input sequence with the memory representations only.
2 code implementations • ACL 2020 • Mor Geva, Ankit Gupta, Jonathan Berant
In this work, we show that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup.
Ranked #9 on Question Answering on DROP Test
1 code implementation • 28 Feb 2020 • Stefan Dvoretskii, Ziyi Gong, Ankit Gupta, Jesse Parent, Bradly Alicea
As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate.
4 code implementations • TACL 2020 • Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan Berant
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
no code implementations • 12 Oct 2019 • Daniele Cappelletti, Ankit Gupta, Mustafa Khammash
These species behave in a stable, predictable way, in the sense that their expression is robust with respect to sudden changes in the species concentration, regardless the new positive equilibrium reached by the system.
no code implementations • 24 Jul 2019 • Ankit Gupta, George Tang, Sylesh Suresh
Using a deep learning network architecture, the database classifies the audio and returns the diagnosis to the user.
no code implementations • 23 Jul 2019 • Ayush Hariharan, Ankit Gupta, Trisha Pal
As machine learning and cybersecurity continue to explode in the context of the digital ecosystem, the complexity of cybersecurity data combined with complicated and evasive machine learning algorithms leads to vast difficulties in designing an end to end system for intelligent, automatic anomaly classification.
no code implementations • 18 Jul 2019 • Ankit Gupta, Kartik Chugh, Andrea Solis, Thomas LaToza
Based off of the results of the user study, user interaction with the rules interface corrects feature relationships missed or mistaken by the automated process, enhancing autocomplete accuracy and developer productivity.
1 code implementation • 17 Jul 2019 • Ankit Gupta, Elliott Ruebush
According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water.
no code implementations • 9 Jul 2019 • Ankit Gupta
StrokeSave is a platform for users to self-diagnose for prevalence to stroke.
1 code implementation • 3 Oct 2017 • Ankit Gupta, Alexander M. Rush
We consider the task of detecting regulatory elements in the human genome directly from raw DNA.
no code implementations • 23 Mar 2015 • Ankit Gupta, Ashish Oberoi
Our approach deals with implementing three decomposition models LMLRA, BTD and CPD to the sample data for choosing the best decomposition of the data set.