Gagandeep Singh

research

∙ 05/31/2023

Incremental Randomized Smoothing Certification

Randomized smoothing-based certification is an effective approach for ob...

0 Shubham Ugare, et al. ∙

research

∙ 05/18/2023

Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning

We propose the first black-box targeted attack against online deep reinf...

0 Yinglun Xu, et al. ∙

research

∙ 04/04/2023

Incremental Verification of Neural Networks

Complete verification of deep neural networks (DNNs) can exactly determi...

0 Shubham Ugare, et al. ∙

research

∙ 03/06/2023

SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation

Fast and accurate climate simulations and weather predictions are critic...

0 Gagandeep Singh, et al. ∙

research

∙ 02/14/2023

COMET: X86 Cost Model Explanation Framework

ML-based program cost models have been shown to yield fairly accurate pr...

1 Isha Chaudhary, et al. ∙

research

∙ 01/31/2023

Interpreting Robustness Proofs of Deep Neural Networks

In recent years numerous methods have been developed to formally verify ...

0 Debangshu Banerjee, et al. ∙

research

∙ 12/09/2022

TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

Basecalling is an essential step in nanopore sequencing analysis where t...

0 Meryem Banu Cavlak, et al. ∙

research

∙ 11/06/2022

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

Nanopore sequencing generates noisy electrical signals that need to be c...

0 Gagandeep Singh, et al. ∙

research

∙ 08/22/2022

LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning

Machine learning has recently gained traction as a way to overcome the s...

11 Gagandeep Singh, et al. ∙

research

∙ 08/18/2022

Designing, Modeling, and Optimizing Data-Intensive Computing Systems

The cost of moving data between the memory units and the compute units i...

0 Gagandeep Singh, et al. ∙

research

∙ 07/22/2022

Training Certifiably Robust Neural Networks Against Semantic Perturbations

Semantic image perturbations, such as scaling and rotation, have been sh...

0 Rem Yang, et al. ∙

research

∙ 07/16/2022

An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System

Training machine learning (ML) algorithms is a computationally intensive...

32 Juan Gomez-Luna, et al. ∙

research

∙ 06/22/2022

Robust Universal Adversarial Perturbations

Universal Adversarial Perturbations (UAPs) are imperceptible, image-agno...

0 Changming Xu, et al. ∙

research

∙ 06/13/2022

Machine Learning Training on a Real Processing-in-Memory System

Training machine learning algorithms is a computationally intensive proc...

42 Juan Gomez-Luna, et al. ∙

research

∙ 05/30/2022

Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning

We study data poisoning attacks on online deep reinforcement learning (D...

0 Yinglun Xu, et al. ∙

research

∙ 05/16/2022

Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis

We now need more than ever to make genome analysis more intelligent. We ...

0 Mohammed Alser, et al. ∙

research

∙ 05/15/2022

Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning

Hybrid storage systems (HSS) use multiple different storage devices to p...

31 Gagandeep Singh, et al. ∙

research

∙ 05/12/2022

SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

A critical step of genome sequence analysis is the mapping of sequenced ...

0 Damla Senol Cali, et al. ∙

research

∙ 03/07/2022

Scalable Verification of GNN-based Job Schedulers

Recently, Graph Neural Networks (GNNs) have been applied for scheduling ...

0 Haoze Wu, et al. ∙

research

∙ 12/28/2021

Casper: Accelerating Stencil Computation using Near-cache Processing

Stencil computation is one of the most used kernels in a wide variety of...

0 Alain Denzler, et al. ∙

research

∙ 10/13/2021

Language Modelling via Learning to Rank

We consider language modelling (LM) as a multi-label structured predicti...

0 Arvid Frydenlund, et al. ∙

research

∙ 09/01/2021

Shared Certificates for Neural Network Verification

Existing neural network verifiers compute a proof that each input is han...

0 Christian Sprecher, et al. ∙

research

∙ 07/19/2021

Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric

Ongoing climate change calls for fast and accurate weather and climate m...

0 Gagandeep Singh, et al. ∙

research

∙ 06/11/2021

FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications

Modern data-intensive applications demand high computation capabilities ...

0 Gagandeep Singh, et al. ∙

research

∙ 03/30/2021

Robustness Certification for Point Cloud Models

The use of deep 3D point cloud models in safety-critical applications, s...

0 Tobias Lorenz, et al. ∙

research

∙ 03/17/2021

Secure Hypersphere Range Query on Encrypted Data

Spatial queries like range queries, nearest neighbor, circular range que...

0 Gagandeep Singh, et al. ∙

research

∙ 03/05/2021

Precise Multi-Neuron Abstractions for Neural Network Certification

Formal verification of neural networks is critical for their safe adopti...

0 Mark Niklas Müller, et al. ∙

research

∙ 02/02/2021

pseudo-Bayesian Neural Networks for detecting Out of Distribution Inputs

Conventional Bayesian Neural Networks (BNNs) are known to be capable of ...

0 Gagandeep Singh, et al. ∙

research

∙ 09/17/2020

NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling

Ongoing climate change calls for fast and accurate weather and climate m...

0 Gagandeep Singh, et al. ∙

research

∙ 07/23/2020

Scalable Inference of Symbolic Adversarial Examples

We present a novel method for generating symbolic adversarial examples: ...

4 Dimitar I. Dimitrov, et al. ∙

research

∙ 07/20/2020

Neural Network Robustness Verification on GPUs

Certifying the robustness of neural networks against adversarial attacks...

26 Christoph Muller, et al. ∙

research

∙ 07/15/2020

Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

Objectives: To predict mechanical ventilation requirement and mortality ...

53 Joseph Bae, et al. ∙

research

∙ 06/30/2020

TDO-CIM: Transparent Detection and Offloading for Computation In-memory

Computation in-memory is a promising non-von Neumann approach aiming at ...

0 Kanishkan Vadivel, et al. ∙

research

∙ 06/16/2020

Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study

A significant challenge in Glioblastoma (GBM) management is identifying ...

0 Marwa Ismail, et al. ∙

research

∙ 05/27/2020

Fast and Effective Robustness Certification for Recurrent Neural Networks

We present a precise and scalable verifier for recurrent neural networks...

23 Wonryong Ryou, et al. ∙

research

∙ 04/20/2020

Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack

Specialized accelerators for tensor-operations, such as blocked-matrix o...

5 Dionysios Diamantopoulos, et al. ∙

research

∙ 03/08/2020

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

We develop an effective generation of adversarial attacks on neural mode...

0 Raphaël Dang-Nhu, et al. ∙

research

∙ 08/07/2019

Near-Memory Computing: Past, Present, and Future

The conventional approach of moving data to the CPU for computation has ...

0 Gagandeep Singh, et al. ∙

research

∙ 06/24/2019

Platform Independent Software Analysis for Near Memory Computing

Near-memory Computing (NMC) promises improved performance for the applic...

0 Stefano Corda, et al. ∙

research

∙ 04/18/2019

Memory and Parallelism Analysis Using a Platform-Independent Approach

Emerging computing architectures such as near-memory computing (NMC) pro...

0 Stefano Corda, et al. ∙

research

∙ 03/29/2019

A Provable Defense for Deep Residual Networks

We present a training system, which can provably defend significantly la...

0 Matthew Mirman, et al. ∙

research

∙ 01/15/2018

Secure k-NN as a Service Over Encrypted Data in Multi-User Setting

To securely leverage the advantages of Cloud Computing, recently a lot o...

0 Gagandeep Singh, et al. ∙

Gagandeep Singh

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