1 citations · 2 across the 8 of their papers we have counts for
8 papers
Learning to Segment using Summary Statistics and Weak Supervision
Omkar Kulkarni, Edward Raff, Tim Oates
Medical experts often manually segment images to obtain diagnostic statistics and discard the resulting annotations. We aim to train segmentation models to alleviate this burden, b…
A Vector Symbolic Approach to Multiple Instance Learning
Ehsan Ahmed Dhrubo, Mohammad Mahmudul Alam, Edward Raff +2
Multiple Instance Learning (MIL) tasks impose a strict logical constraint: a bag is labeled positive if and only if at least one instance within it is positive. While this iff cons…
DeBUGCN -- Detecting Backdoors in CNNs Using Graph Convolutional Networks
Akash Vartak, Khondoker Murad Hossain, Tim Oates
Deep neural networks (DNNs) are becoming commonplace in critical applications, making their susceptibility to backdoor (trojan) attacks a significant problem. In this paper, we int…
A Walsh Hadamard Derived Linear Vector Symbolic Architecture
Mohammad Mahmudul Alam, Alexander Oberle, Edward Raff +3
Vector Symbolic Architectures (VSAs) are one approach to developing Neuro-symbolic AI, where two vectors in are `bound' together to produce a new vector in the same…
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Mohammad Mahmudul Alam, Edward Raff, Stella Biderman +2
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges. We investigate existing long…
Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks
Khondoker Murad Hossain, Tim Oates
In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a si…
Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations
Mohammad Mahmudul Alam, Edward Raff, Tim Oates
While deep learning has enjoyed significant success in computer vision tasks over the past decade, many shortcomings still exist from a Cognitive Science (CogSci) perspective. In p…
DDxT: Deep Generative Transformer Models for Differential Diagnosis
Mohammad Mahmudul Alam, Edward Raff, Tim Oates +1
Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An…