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Found 9 papers, 3 papers with code
Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System
In this work, we carry out a systematic study of the following: (i) different patterns of missing values, (ii) various statistical and ML based data imputation methods for different kinds of sparsity, (iii) several concept drift detection methods, (iv) practical analysis of the various drift detection metrics, (v) selecting the best concept drift detector given a dataset with missing values based on the different metrics.
Look Before You Leap! Designing a Human-Centered AI System for Change Risk Assessment
This warrants the development of an automated system to assess the risk associated with a large number of changes.
Exploring Alternatives to Softmax Function
Softmax function is widely used in artificial neural networks for multiclass classification, multilabel classification, attention mechanisms, etc.
K-TanH: Efficient TanH For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning.
High-Performance Deep Learning via a Single Building Block
Deep learning (DL) is one of the most prominent branches of machine learning.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image recognition, neural machine translation and speech recognition.
Distributed, Parallel, and Cluster Computing
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017).
Ternary Residual Networks
Aided by such an elegant trade-off between accuracy and compute, the 8-2 model (8-bit activations, ternary weights), enhanced by ternary residual edges, turns out to be sophisticated enough to achieve very high accuracy ($\sim 1\%$ drop from our FP-32 baseline), despite $\sim 1. 6\times$ reduction in model size, $\sim 26\times$ reduction in number of multiplications, and potentially $\sim 2\times$ power-performance gain comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset.
