Search Results for author:
Found 17 papers, 1 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Textbooks Are All You Need
Despite this small scale, phi-1 attains pass@1 accuracy 50. 6% on HumanEval and 55. 5% on MBPP.
Ranked #41 on
Code Generation
on HumanEval
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Experimental results demonstrate that NetFlick can successfully deteriorate the performance of video compression frameworks in both digital- and physical-settings and can be further extended to attack downstream video classification networks.
zPROBE: Zero Peek Robustness Checks for Federated Learning
However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected.
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression
In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems.
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Results show that the perplexity of 16-layer GPT-2 and Transformer-XL can be achieved with up to 1. 5x, 2. 5x faster runtime and 1. 2x, 2. 0x lower peak memory utilization.
Machine Learning-Assisted E-jet Printing of Organic Flexible Biosensors
Electrohydrodynamic-jet (e-jet) printing technique enables the high-resolution printing of complex soft electronic devices.
HASHTAG: Hash Signatures for Online Detection of Fault-Injection Attacks on Deep Neural Networks
We propose HASHTAG, the first framework that enables high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance.
Trojan Signatures in DNN Weights
Deep neural networks have been shown to be vulnerable to backdoor, or trojan, attacks where an adversary has embedded a trigger in the network at training time such that the model correctly classifies all standard inputs, but generates a targeted, incorrect classification on any input which contains the trigger.
CLEANN: Accelerated Trojan Shield for Embedded Neural Networks
We propose CLEANN, the first end-to-end framework that enables online mitigation of Trojans for embedded Deep Neural Network (DNN) applications.
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution
We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions; (2) sampling examples from an approximation to the underlying data distribution; and (3) matching student and teacher output distributions over this extended set including uncertain samples.
GeneCAI: Genetic Evolution for Acquiring Compact AI
In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy.
FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA
While WaveNet produces state-of-the art audio generation results, the naive inference implementation is quite slow; it takes a few minutes to generate just one second of audio on a high-end GPU.
ASCAI: Adaptive Sampling for acquiring Compact AI
This paper introduces ASCAI, a novel adaptive sampling methodology that can learn how to effectively compress Deep Neural Networks (DNNs) for accelerated inference on resource-constrained platforms.
SWNet: Small-World Neural Networks and Rapid Convergence
This transformation leverages our important observation that for a set level of accuracy, convergence is fastest when network topology reaches the boundary of a Small-World Network.
CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs
CodeX incorporates nonlinear encoding to the computation flow of neural networks to save memory.
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning
Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems.
