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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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publications
Cache Miss Rate Predictability via Neural Networks
Published in NeurIPS 2018 Workshop on ML in Systems, 2018
Abstract
A program run, in the setting of computer architecture and compilers, can be characterized in part by its memory access patterns. We approach the problem of analyzing these patterns using machine learning. We characterize memory accesses using a sequence of cache miss rates, and present a new data set for this task. The data set draws from programs run on various Java virtual machines, and C and Fortran compilers. We work towards answering the scientific question: How predictable is a program’s cache miss rate from interval to interval as it executes? We report the results of three distinct ANN models, which have been shown to be effective in sequence modeling. We show that programs can be differentiated in terms of the predictability of their cache miss rates.
Recommended citation: Rishikesh Jha, Saket Tiwari, Arjun Kuravally, Eliot Moss
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Published in , 2020
Abstract
Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still requires large amounts of taskspecific labelled data to achieve good performance. We consider this problem of learning to generalize to new tasks with few examples as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited diversity across tasks. We develop a novel method, LEOPARD, which enables optimization-based meta-learning across tasks with different number of classes, and evaluate existing methods on generalization to diverse NLP classification tasks. LEOPARD is trained with the state-of-the-art transformer architecture and shows strong generalization to tasks not seen at all during training, with as few as 8 examples per label. On 16 NLP datasets, across a diverse task-set such as entity typing, relation extraction, natural language inference, sentiment analysis, and several other text categorization tasks, we show that LEOPARD learns better initial parameters for few-shot learning than self-supervised pretraining or multi-task training, outperforming many strong baselines, for example, increasing F1 from 49% to 72%.
Recommended citation: Rishikesh Jha, Trapit Bansal, Andrew McCallum
Emission-aware Energy Storage Scheduling for a Greener Grid
Published in , 2020
Abstract
Abstract—Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid’s carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions and intermittent renewables. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid. Our results show a reduction of >0.5 million kg in annual carbon emissions — equivalent to a drop of 23.3% in our electric grid emissions.
Recommended citation: Rishikesh Jha Stephen Lee, Srinivasan Iyengar, Mohammad Hajiesmali, Prashant Shenoy, David Irwin
Meta-Learned task weights for improved multitask learning (Ongoing Work)
Published in , 1900
Learning task importance weights during multitask learning for improved performance on target task.
Recommended citation: Rishikesh Jha
talks
Burning Glass Technologies: Skill Extraction and Building a Skill Knowledge Graph
Published:
Poster Presentation on workforce skill extraction and knowledge graph creation. Work done in collaboration with industry partners Burning Glass Technologies.
[Poster]
Emissions Aware Distributed Energy Scheduling
Published:
Short Talk on distributed storage scheduling to reduce greenhouse gas emission in a smart grid.
[Slides]
Cache Miss Rate Predictability via Neural Networks
Published:
Poster Presentation
[Poster]
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.