Search Results for author:
Found 9 papers, 2 papers with code
LEANN: A Low-Storage Vector Index
Embedding-based search is widely used in applications such as recommendation and retrieval-augmented generation (RAG).
Qwen2.5-1M Technical Report
By leveraging our inference framework, the Qwen2. 5-1M models achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million tokens of context.
NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics
Our presented NetFlowGen framework goes beyond a proof-of-concept for network traffic pre-training and addresses specific challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection.
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization
Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e. g., each subgraph can only contain at most one complex operator.
Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale.
ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations
Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware.
Automating Botnet Detection with Graph Neural Networks
Botnets are now a major source for many network attacks, such as DDoS attacks and spam.
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning
ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise.
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
