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Found 8 papers, 6 papers with code
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
We demonstrate that it is possible to fine-tune both ResNet-18 and BERT within the memory constraints of a Cortex-M class embedded device while outperforming current edge training methods in energy efficiency.
Representing Long-Range Context for Graph Neural Networks with Global Attention
Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module.
Grounded Graph Decoding Improves Compositional Generalization in Question Answering
Grounding enables the model to retain syntax information from the input in thereby significantly improving generalization over complex inputs.
Accelerating Quadratic Optimization with Reinforcement Learning
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Contrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context.
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Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies.
The OoO VLIW JIT Compiler for GPU Inference
Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e. g., GPUs, TPUs) point to alarmingly low utilization.
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning
Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR).
