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Found 4 papers, 0 papers with code
HetHub: A Heterogeneous distributed hybrid training system for large-scale models
To address the problem, this paper proposes a distributed training system with hybrid parallelism support on heterogeneous GPU-accelerators for large-scale models.
Learning Minimal NAP Specifications for Neural Network Verification
In this paper, we study the following problem: Given a neural network, find a minimal (coarsest) NAP that is sufficient for formal verification of the network's robustness.
Scalar Invariant Networks with Zero Bias
However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles.
Novice Type Error Diagnosis with Natural Language Models
Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations.
