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Found 15 papers, 6 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.
Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends
The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it.
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability.
Adaptive Sampling for Linear Sensing Systems via Langevin Dynamics
Adaptive or dynamic signal sampling in sensing systems can adapt subsequent sampling strategies based on acquired signals, thereby potentially improving image quality and speed.
Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)
Optimizing 3D k-space sampling trajectories for efficient MRI is important yet challenging.
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control
The proposed scheme does not require pre-computation and can improve the amortized running time of the composed MPC with a well-trained neural network.
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
In fact, we show that model-based image reconstruction (MBIR) methods with suitably optimized imaging parameters can perform nearly as well as CNN-based methods.
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction
We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.
B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop.
Blink: Fast and Generic Collectives for Distributed ML
Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale.
KLEESPECTRE: Detecting Information Leakage through Speculative Cache Attacks via Symbolic Execution
Spectre attacks disclosed in early 2018 expose data leakage scenarios via cache side channels.
Cryptography and Security
Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification
Our analysis of the classification results suggests that Cyber threat relevant tweets on Twitter do not often include the CVE identifier of the related threats.
Task-GAN for Improved GAN based Image Restoration
Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration.
oo7: Low-overhead Defense against Spectre Attacks via Program Analysis
In this paper, we propose oo7, a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them.
Cryptography and Security
