494 citations · 623 across the 25 of their papers we have counts for
26 papers
CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queries
Pratyush Kumar Shukla, Manveer Singh Tib, Siddhant Garg
A natural language interface can be used to make cancer genomics databases easier to use, but even if a question is perfectly fluent, its scientific meaning can be ambiguous. We pr…
Evaluating Conformal Reliability of Pathway-Level Transcriptomic Signatures Under Cross-Cohort Shift in Sepsis Mortality Prediction
Pratyush Kumar Shukla, Manveer Singh Tib, Siddhant Garg
Blood transcriptomic profiling enables prognostic modeling by capturing the host immune response at the molecular level. Yet, the within-cohort evaluation strategies employed by ma…
Memory-QA: Answering Recall Questions Based on Multimodal Memories
Hongda Jiang, Xinyuan Zhang, Siddhant Garg +10
We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique cha…
ArchXBench: A Complex Digital Systems Benchmark Suite for LLM Driven RTL Synthesis
Suresh Purini, Siddhant Garg, Mudit Gaur +3
Modern SoC datapaths include deeply pipelined, domain-specific accelerators, but their RTL implementation and verification are still mostly done by hand. While large language model…
Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg +2
In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on t…
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski +1
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM)…
Context-Aware Transformer Pre-Training for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Alessandro Moschitti
Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline. AS2 models rank a set of candidate sentences based on how likely they answ…
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators
Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski +1
Recent studies show that sentence-level extractive QA, i.e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers…
Structured Pruning for Multi-Task Deep Neural Networks
Siddhant Garg, Lijun Zhang, Hui Guan
Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compressi…
Knowledge Transfer from Answer Ranking to Answer Generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg +2
Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (t…
SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds
Siddhant Garg, Mudit Chaudhary
We present SeRP, a framework for Self-Supervised Learning of 3D point clouds. SeRP consists of encoder-decoder architecture that takes perturbed or corrupted point clouds as inputs…
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved…
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. R…
Self-Labeling Refinement for Robust Representation Learning with Bootstrap Your Own Latent
Siddhant Garg, Dhruval Jain
In this work, we have worked towards two major goals. Firstly, we have investigated the importance of Batch Normalisation (BN) layers in a non-contrastive representation learning f…
Attentive Walk-Aggregating Graph Neural Networks
Mehmet F. Demirel, Shengchao Liu, Siddhant Garg +2
Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecu…
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering
Siddhant Garg, Alessandro Moschitti
In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is…
Towards Robustness to Label Noise in Text Classification via Noise Modeling
Siddhant Garg, Goutham Ramakrishnan, Varun Thumbe
Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to…
Can Adversarial Weight Perturbations Inject Neural Backdoors?
Siddhant Garg, Adarsh Kumar, Vibhor Goel +1
Adversarial machine learning has exposed several security hazards of neural models and has become an important research topic in recent times. Thus far, the concept of an "adversar…
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Siddhant Garg, Yingyu Liang
Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks. While these approaches are widely used in…
Advances in Quantum Deep Learning: An Overview
Siddhant Garg, Goutham Ramakrishnan
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing…
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer
Siddhant Garg, Rohit Kumar Sharma, Yingyu Liang
Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. In this paper we show that concatenating the embeddings from the pre-tr…
BAE: BERT-based Adversarial Examples for Text Classification
Siddhant Garg, Goutham Ramakrishnan
Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Rec…
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
Marios Loizou, Siddhant Garg, Dmitry Petrov +2
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-…
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
Siddhant Garg, Thuy Vu, Alessandro Moschitti
We propose TANDA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model…