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Found 12 papers, 4 papers with code
ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation, exiting early out of deep models and so on.
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Further, batching puts strict restrictions on memory adjacency and can lead to high data movement costs.
Federated Learning under Distributed Concept Drift
Federated Learning (FL) under distributed concept drift is a largely unexplored area.
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
There is often variation in the shape and size of input data used for deep learning.
Cortex: A Compiler for Recursive Deep Learning Models
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those found in vendor libraries.
DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift
The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
The Non-IID Data Quagmire of Decentralized Machine Learning
Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing significant accuracy loss across many ML applications, DNN models, training datasets, and decentralized learning algorithms; (ii) the problem is particularly challenging for DNN models with batch normalization; and (iii) the degree of data skew is a key determinant of the difficulty of the problem.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
MLtuner: System Support for Automatic Machine Learning Tuning
MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impact on large-scale machine learning (ML) performance.
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.
