research
∙
09/21/2023
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
Programmers and researchers are increasingly developing surrogates of pr...
share
research
∙
07/14/2023
Verifying Performance Properties of Probabilistic Inference
In this extended abstract, we discuss the opportunity to formally verify...
research
∙
04/28/2023
Quantum Control Machine: The Limits of Quantum Programs as Data
Quantum algorithms for factorization, search, and simulation obtain comp...
research
∙
03/09/2023
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable
In reinforcement learning, the classic objectives of maximizing discount...
research
∙
12/10/2022
Acela: Predictable Datacenter-level Maintenance Job Scheduling
Datacenter operators ensure fair and regular server maintenance by using...
research
∙
10/25/2022
Pruning's Effect on Generalization Through the Lens of Training and Regularization
Practitioners frequently observe that pruning improves model generalizat...
research
∙
09/15/2022
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming
Efficient inference is often possible in a streaming context using Rao-B...
research
∙
05/20/2022
Tower: Data Structures in Quantum Superposition
Emerging quantum algorithms for problems such as element distinctness, s...
research
∙
05/04/2022
Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs
Quantum programming languages enable developers to implement algorithms ...
research
∙
04/22/2022
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization
Modern computer systems need to execute under strict safety constraints ...
research
∙
04/11/2022
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Sample-efficient machine learning (SEML) has been widely applied to find...
research
∙
12/12/2021
Programming with Neural Surrogates of Programs
Surrogates, models that mimic the behavior of programs, form the basis o...
research
∙
11/24/2021
Reinforcement Learning for General LTL Objectives Is Intractable
In recent years, researchers have made significant progress in devising ...
research
∙
09/26/2021
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams
Probabilistic programming languages aid developers performing Bayesian i...
research
∙
04/30/2021
Studying the Consistency and Composability of Lottery Ticket Pruning Masks
Magnitude pruning is a common, effective technique to identify sparse su...
research
∙
01/12/2021
Programming and Reasoning with Partial Observability
Computer programs are increasingly being deployed in partially-observabl...
research
∙
12/12/2020
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
The computer vision world has been re-gaining enthusiasm in various pre-...
research
∙
10/08/2020
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
CPU simulators are useful tools for modeling CPU execution behavior. How...
research
∙
09/18/2020
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Recent work has explored the possibility of pruning neural networks at i...
research
∙
07/23/2020
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
In natural language processing (NLP), enormous pre-trained models like B...
research
∙
07/22/2020
Simplifying Multiple-Statement Reductions with the Polyhedral Model
A Reduction – an accumulation over a set of values, using an associative...
research
∙
07/15/2020
λ_S: Computable semantics for differentiable programming with higher-order functions and datatypes
Deep learning is moving towards increasingly sophisticated optimization ...
research
∙
06/18/2020
On the Predictability of Pruning Across Scales
We show that the error of magnitude-pruned networks follows a scaling la...
research
∙
05/07/2020
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
In this paper, we demonstrate a compiler that can optimize sparse and re...
research
∙
03/05/2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Many neural network pruning algorithms proceed in three steps: train the...
research
∙
12/11/2019
Linear Mode Connectivity and the Lottery Ticket Hypothesis
We introduce "instability analysis," a framework for assessing whether t...
research
∙
08/20/2019
Reactive Probabilistic Programming
Synchronous reactive languages were introduced for designing and impleme...
research
∙
03/05/2019
The Lottery Ticket Hypothesis at Scale
Recent work on the "lottery ticket hypothesis" proposes that randomly-in...
research
∙
09/16/2018
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms
When a computational task tolerates a relaxation of its specification or...
research
∙
08/21/2018
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Statically estimating the number of processor clock cycles it takes to e...
research
∙
05/16/2018
Verifying Programs Under Custom Application-Specific Execution Models
Researchers have recently designed a number of application-specific faul...
research
∙
05/04/2018
Verifying Handcoded Probabilistic Inference Procedures
Researchers have recently proposed several systems that ease the process...
research
∙
05/01/2018
Computable decision making on the reals and other spaces via partiality and nondeterminism
Though many safety-critical software systems use floating point to repre...
research
∙
03/20/2018
The Three Pillars of Machine Programming
In this position paper, we describe our vision of the future of machine ...
research
∙
03/20/2018
The Three Pillars of Machine-Based Programming
In this position paper, we describe our vision of the future of machine-...
research
∙
03/09/2018
The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks
Neural network compression techniques are able to reduce the parameter c...
research
∙
03/09/2018