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Found 13 papers, 10 papers with code
TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs
On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads.
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048.
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
We then propose to search for the optimal per-channel scaling that protects the salient weights by observing the activation, not weights.
SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer
High-resolution images enable neural networks to learn richer visual representations.
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
Transformer, as an alternative to CNN, has been proven effective in many modalities (e. g., texts and images).
End-to-End Entity Detection with Proposer and Regressor
Named entity recognition is a traditional task in natural language processing.
Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition
Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Ranked #4 on
3D Object Detection
on nuScenes
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution
3D neural networks are widely used in real-world applications (e. g., AR/VR headsets, self-driving cars).
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition.
TorchSparse: Efficient Point Cloud Inference Engine
TorchSparse directly optimizes the two bottlenecks of sparse convolution: irregular computation and data movement.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
Ranked #1 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Point-Voxel CNN for Efficient 3D Deep Learning
The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.
Ranked #1 on
3D Object Detection
on KITTI Pedestrian Hard val
