Search Results for author:
Found 12 papers, 3 papers with code
MLPerf Training Benchmark
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
Device Placement Optimization with Reinforcement Learning
Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.
Multimodal Representation Learning using Deep Multiset Canonical Correlation
We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities.
In-Datacenter Performance Analysis of a Tensor Processing Unit
Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand.
Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform
Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains.
Scale MLPerf-0.6 models on Google TPU-v3 Pods
The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0. 6 training benchmark demonstrates the scalability of a suite of industry relevant ML models.
RNN based Incremental Online Spoken Language Understanding
We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Quantized deep learning models on low-power edge devices for robotic systems
In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space.
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images
Further, LIME has been integrated with the proposed COV-ELM model to generate annotated CXR images.
Exploring the limits of Concurrency in ML Training on Google TPUs
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.
Deep Learning Based Model for Breast Cancer Subtype Classification
By deploying the combined network of stages 1 and 2, we have been able to attain a mean 10-fold test accuracy of 0. 907 on the TCGA breast cancer dataset.
Biomarker Gene Identification for Breast Cancer Classification
CONCLUSIONS: The proposed technique enables us to find a concise and clinically relevant gene signature set.
