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Found 36 papers, 20 papers with code
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware.
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses.
Discovering Predictable Latent Factors for Time Series Forecasting
Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems.
Q-Diffusion: Quantizing Diffusion Models
Diffusion models have achieved great success in synthesizing diverse and high-fidelity images.
Data-Driven Load-Current Sharing Control for Multi-Stack Fuel Cell System with Circulating Current Mitigation
The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications.
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
CSQ stabilizes the bit-level mixed-precision training process with a bi-level gradual continuous sparsification on both the bit values of the quantized weights and the bit selection in determining the quantization precision of each layer.
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer.
Analysis of Quantization on MLP-based Vision Models
Quantization is wildly taken as a model compression technique, which obtains efficient models by converting floating-point weights and activations in the neural network into lower-bit integers.
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Then during training, DASK injects pivot-related knowledge graph information into source domain texts.
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs).
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement
This paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation Control (AGC) enhancement, i. e., the optimal coordination of multiple Battery Energy Storage Systems (BESSs).
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image.
KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
The student network takes the incomplete one as input and restores the corresponding complete shape.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface.
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points.
Ranked #4 on
Surface Normals Estimation
on PCPNet
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference
Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs.
Hardware Aware Neural Architecture Search
Image Classification
+2
A Survey of Quantization Methods for Efficient Neural Network Inference
Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks.
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds.
Hessian-Aware Pruning and Optimal Neural Implant
To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled with a Neural Implant approach that uses second-order sensitivity as a metric for structured pruning.
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
In this paper, we propose a contrastive learning framework for cross-domain sentiment classification.
Learnable Motion Coherence for Correspondence Pruning
We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian.
HAWQV3: Dyadic Neural Network Quantization
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values.
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
Due to scarcity of labels on the target domain, we introduce mutual information maximization (MIM) apart from CL to exploit the features that best support the final prediction.
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Deploying deep learning models on embedded systems has been challenging due to limited computing resources.
Algorithm-hardware Co-design for Deformable Convolution
In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.
ZeroQ: A Novel Zero Shot Quantization Framework
Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0. 5\% of one epoch training time of ResNet50 on ImageNet).
Ranked #1 on
Data Free Quantization
on CIFAR10
(CIFAR-10 W8A8 Top-1 Accuracy metric)
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
However, the search space for a mixed-precision quantization is exponential in the number of layers.
Data-Anonymous Encoding for Text-to-SQL Generation
On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called \textit{table-related tokens}.
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
In particular, we propose a new group-wise quantization scheme, and we use a Hessian based mix-precision method to compress the model further.
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
Another challenge is a similar factorial complexity for determining block-wise fine-tuning order when quantizing the model to a target precision.
Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality
While external pose estimation and fiducial marker based localization would require setup, maintenance, and manual calibration; marker-free self-localization can be achieved using the onboard depth sensor and camera.
Robotics
Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration
Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry.
Input Aggregated Network for Face Video Representation
To sufficiently discover the useful information contained in face videos, we present a novel network architecture called input aggregated network which is able to learn fixed-length representations for variable-length face videos.
