Daniel Kang | DeepAI

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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...

0 Clark Barrett, et al. ∙

∙ 08/17/2023

Accelerating Aggregation Queries on Unstructured Streams of Data

Analysts and scientists are interested in querying streams of video, aud...

0 Matthew Russo, et al. ∙

∙ 03/28/2023

Dias: Dynamic Rewriting of Pandas Code

In recent years, dataframe libraries, such as pandas have exploded in po...

0 Stefanos Baziotis, et al. ∙

∙ 02/11/2023

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Recent advances in instruction-following large language models (LLMs) ha...

0 Daniel Kang, et al. ∙

∙ 02/08/2023

Q-Diffusion: Quantizing Diffusion Models

Diffusion models have achieved great success in synthesizing diverse and...

0 Xiuyu Li, et al. ∙

∙ 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...

0 Daniel Kang, et al. ∙

∙ 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...

0 Daniel Kang, et al. ∙

∙ 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 ...

0 Daniel Kang, et al. ∙

∙ 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...

0 Daniel Kang, et al. ∙

∙ 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...

1 Daniel Kang, et al. ∙

∙ 04/30/2020

Improved Natural Language Generation via Loss Truncation

Neural language models are usually trained to match the distributional p...

0 Daniel Kang, et al. ∙

∙ 04/02/2020

Approximate Selection with Guarantees using Proxies

Due to the falling costs of data acquisition and storage, researchers an...

0 Daniel Kang, et al. ∙

∙ 03/03/2020

Model Assertions for Monitoring and Improving ML Model

ML models are increasingly deployed in settings with real world interact...

16 Daniel Kang, et al. ∙

∙ 03/03/2020

Model Assertions for Monitoring and Improving ML Models

ML models are increasingly deployed in settings with real world interact...

9 Daniel Kang, et al. ∙

∙ 10/02/2019

MLPerf Training Benchmark

Machine learning is experiencing an explosion of software and hardware s...

6 Peter Mattson, et al. ∙

∙ 08/21/2019

Testing Robustness Against Unforeseen Adversaries

Considerable work on adversarial defense has studied robustness to a fix...

1 Daniel Kang, et al. ∙

∙ 06/03/2019

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference

Machine learning (ML) has become increasingly important and performance-...

0 Peter Kraft, et al. ∙

∙ 05/03/2019

Transfer of Adversarial Robustness Between Perturbation Types

We study the transfer of adversarial robustness of deep neural networks ...

12 Daniel Kang, et al. ∙

∙ 02/15/2019

Network Offloading Policies for Cloud Robotics: a Learning-based Approach

Today's robotic systems are increasingly turning to computationally expe...

0 Sandeep Chinchali, et al. ∙

∙ 10/02/2018

LIT: Block-wise Intermediate Representation Training for Model Compression

Knowledge distillation (KD) is a popular method for reducing the computa...

4 Animesh Koratana, et al. ∙

∙ 06/04/2018

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

The deep learning community has proposed optimizations spanning hardware...

0 Cody Coleman, et al. ∙

∙ 05/02/2018

BlazeIt: Fast Exploratory Video Queries using Neural Networks

As video volumes grow, analysts have increasingly turned to deep learnin...

0 Daniel Kang, et al. ∙

∙ 03/07/2017

NoScope: Optimizing Neural Network Queries over Video at Scale

Recent advances in computer vision-in the form of deep neural networks-h...

0 Daniel Kang, et al. ∙

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