Search Results for author:
Found 14 papers, 5 papers with code
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
In this study, we explore the feasibility of end-to-end training of a hybrid model with a black-box PDE solver and a deep learning model for fluid flow prediction.
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
However, our scaling laws also predict robustness slowly grows then plateaus at $90$%: dwarfing our new SOTA by scaling is impractical, and perfect robustness is impossible.
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected.
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial.
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
However, advanced ANN-to-SNN conversion approaches demonstrate that for lossless conversion, the number of SNN time steps must equal the number of quantization steps in the ANN activation function.
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time.
Neural Image Compression: Generalization, Robustness, and Spectral Biases
Our benchmarks, spectral inspection tools, and findings provide a crucial bridge to the real-world adoption of NIC.
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks.
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
However, there still is no clear understanding of the conditions on OOD data and model properties that are required to observe effective robustness.
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning
Existing knowledge integration approaches are limited to using differentiable knowledge source to be compatible with first-order DL training paradigm.
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
To improve robustness of DNNs, they must be able to update themselves to enhance their prediction accuracy.
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts.
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
In this paper, we propose (and prove) a stronger Multi-Prize Lottery Ticket Hypothesis: A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i. e., binary weights and/or activation) (prize 3).
Classification with Binary Neural Network
Classification with Binary Weight Network
+1
Multi-Prize Lottery Ticket Hypothesis: Finding Generalizable and Efficient Binary Subnetworks in a Randomly Weighted Neural Network
A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i. e., binary weights and/or activation) (prize 3).
