10 citations · 18 across the 12 of their papers we have counts for
12 papers
Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research
Mirazul Haque, Antony Papadimitriou, Samuel Mensah +6
We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research. The benchmark assesses three…
Detecting Non-Membership in LLM Training Data via Rank Correlations
Pranav Shetty, Mirazul Haque, Zhiqiang Ma +1
As large language models (LLMs) are trained on increasingly vast and opaque text corpora, determining which data contributed to training has become essential for copyright enforcem…
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin +3
Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We pre…
Efficiency Robustness of Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Tingxi Li, Zexin Xu +4
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address eff…
Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs
Mirazul Haque, Petr Babkin, Farima Farmahinifarahani +1
Large Language Models (LLMs) show promising performance on various programming tasks, including Automatic Program Repair (APR). However, most approaches to LLM-based APR are limite…
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks
Mirazul Haque, Wei Yang
Deep Neural Networks (DNNs) have been used to solve different day-to-day problems. Recently, DNNs have been deployed in real-time systems, and lowering the energy consumption and r…
SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
Mirazul Haque, Rutvij Shah, Simin Chen +3
Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in different real…
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
Simin Chen, Mirazul Haque, Cong Liu +1
Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used on resource-constrained embedded devices. We observe that, similar to traditional software, redu…
TestAug: A Framework for Augmenting Capability-based NLP Tests
Guanqun Yang, Mirazul Haque, Qiaochu Song +2
The recently proposed capability-based NLP testing allows model developers to test the functional capabilities of NLP models, revealing functional failures that cannot be detected…
CorrGAN: Input Transformation Technique Against Natural Corruptions
Mirazul Haque, Christof J. Budnik, Wei Yang
Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial pertu…
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
Simin Chen, Zihe Song, Mirazul Haque +2
Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work…
EREBA: Black-box Energy Testing of Adaptive Neural Networks
Mirazul Haque, Yaswanth Yadlapalli, Wei Yang +1
Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determini…