Search Results for author:
Found 27 papers, 18 papers with code
Condition-Aware Neural Network for Controlled Image Generation
In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neural network.
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step.
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT.
EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction
Without performance loss on Cityscapes, our EfficientViT provides up to 8. 8x and 3. 8x GPU latency reduction over SegFormer and SegNeXt, respectively.
Decentralised possibilistic inference with applications to target tracking
Fusing and sharing information from multiple sensors over a network is a challenging task.
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Without performance loss on Cityscapes, our EfficientViT provides up to 13. 9$\times$ and 6. 2$\times$ GPU latency reduction over SegFormer and SegNeXt, respectively.
Ranked #23 on
Semantic Segmentation
on Cityscapes val
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.
Ranked #5 on
Multi-Person Pose Estimation
on MS COCO
(Validation AP metric)
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.
Memory-efficient Patch-based Inference for Tiny Deep Learning
We further propose receptive field redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.
Network Augmentation for Tiny Deep Learning
We introduce Network Augmentation (NetAug), a new training method for improving the performance of tiny neural networks.
TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
Furthermore, combined with feature extractor adaptation, TinyTL provides 7. 3-12. 9x memory saving without sacrificing accuracy compared to fine-tuning the full Inception-V3.
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
However, training this quantization-aware accuracy predictor requires collecting a large number of quantized <model, accuracy> pairs, which involves quantization-aware finetuning and thus is highly time-consuming.
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search.
Ranked #21 on
Machine Translation
on WMT2014 English-French
Once for All: Train One Network and Specialize it for Efficient Deployment
Most of the traditional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Once-for-All: Train One Network and Specialize it for Efficient Deployment
On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4. 0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1. 5x faster than MobileNetV3, 2. 6x faster than EfficientNet w. r. t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission.
Ranked #76 on
Neural Architecture Search
on ImageNet
Design Automation for Efficient Deep Learning Computing
Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms.
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set.
Ranked #6 on
Neural Architecture Search
on CIFAR-10 Image Classification
(using extra training data)
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
Path-Level Network Transformation for Efficient Architecture Search
We introduce a new function-preserving transformation for efficient neural architecture search.
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Ranked #1 on
Text Generation
on COCO Captions
Efficient Architecture Search by Network Transformation
Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results.
Ranked #140 on
Image Classification
on CIFAR-10
Activation Maximization Generative Adversarial Nets
Our proposed model also outperforms the baseline methods in the new metric.
Real-Time Bidding by Reinforcement Learning in Display Advertising
In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Ranked #1 on
Click-Through Rate Prediction
on iPinYou
