11 citations · 11 across the 5 of their papers we have counts for
10 papers
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
Nathan Drenkow, Alvin Tan, Chace Ashcraft +1
The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision mod…
Machine Learning aided Crop Yield Optimization
Chace Ashcraft, Kiran Karra
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL…
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
Chace Ashcraft, Kiran Karra
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are tri…
Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings
Kiran Karra, Alan McCree
Many modern systems for speaker diarization, such as the recently-developed VBx approach, rely on clustering of DNN speaker embeddings followed by resegmentation. Two problems with…
Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation
Yijun Xu, Kiran Karra, Lamine Mili +3
The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of powe…
The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
Kiran Karra, Chace Ashcraft, Neil Fendley
In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL)…