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08/28/2023
Identifying and Mitigating the Security Risks of Generative AI
Every major technical invention resurfaces the dual-use dilemma – the ne...
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08/17/2023
Accelerating Aggregation Queries on Unstructured Streams of Data
Analysts and scientists are interested in querying streams of video, aud...
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03/28/2023
Dias: Dynamic Rewriting of Pandas Code
In recent years, dataframe libraries, such as pandas have exploded in po...
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02/11/2023
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Recent advances in instruction-following large language models (LLMs) ha...
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02/08/2023
Q-Diffusion: Quantizing Diffusion Models
Diffusion models have achieved great success in synthesizing diverse and...
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11/09/2022
ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation
Over the past few years, AI methods of generating images have been incre...
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10/17/2022
Scaling up Trustless DNN Inference with Zero-Knowledge Proofs
As ML models have increased in capabilities and accuracy, so has the com...
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01/15/2022
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
ML is being deployed in complex, real-world scenarios where errors have ...
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09/09/2020
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data
Unstructured data is now commonly queried by using target deep neural ne...
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07/25/2020
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
While deep neural networks (DNNs) are an increasingly popular way to que...
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04/30/2020
Improved Natural Language Generation via Loss Truncation
Neural language models are usually trained to match the distributional p...
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04/02/2020
Approximate Selection with Guarantees using Proxies
Due to the falling costs of data acquisition and storage, researchers an...
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03/03/2020
Model Assertions for Monitoring and Improving ML Model
ML models are increasingly deployed in settings with real world interact...
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03/03/2020
Model Assertions for Monitoring and Improving ML Models
ML models are increasingly deployed in settings with real world interact...
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10/02/2019
MLPerf Training Benchmark
Machine learning is experiencing an explosion of software and hardware s...
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08/21/2019
Testing Robustness Against Unforeseen Adversaries
Considerable work on adversarial defense has studied robustness to a fix...
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06/03/2019
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Machine learning (ML) has become increasingly important and performance-...
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05/03/2019
Transfer of Adversarial Robustness Between Perturbation Types
We study the transfer of adversarial robustness of deep neural networks ...
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02/15/2019
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Today's robotic systems are increasingly turning to computationally expe...
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10/02/2018
LIT: Block-wise Intermediate Representation Training for Model Compression
Knowledge distillation (KD) is a popular method for reducing the computa...
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06/04/2018
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
The deep learning community has proposed optimizations spanning hardware...
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05/02/2018
BlazeIt: Fast Exploratory Video Queries using Neural Networks
As video volumes grow, analysts have increasingly turned to deep learnin...
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03/07/2017