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Found 15 papers, 4 papers with code
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations.
Graph Attention-Based Symmetry Constraint Extraction for Analog Circuits
In recent years, analog circuits have received extensive attention and are widely used in many emerging applications.
AiDAC: A Low-Cost In-Memory Computing Architecture with All-Analog Multi-Bit Compute and Interconnect
Analog in-memory computing (AiMC) is an emerging technology that shows fantastic performance superiority for neural network acceleration.
NicePIM: Design Space Exploration for Processing-In-Memory DNN Accelerators with 3D-Stacked-DRAM
As one of the feasible processing-in-memory(PIM) architectures, 3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solution for DNN accelerators with better performance and energy efficiency.
Continuous Cross-resolution Remote Sensing Image Change Detection
Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences.
Accelerated Distributed Aggregative Optimization
In this paper, we investigate a distributed aggregative optimization problem in a network, where each agent has its own local cost function which depends not only on the local state variable but also on an aggregated function of state variables from all agents.
Language-aware Multiple Datasets Detection Pretraining for DETRs
Pretraining on large-scale datasets can boost the performance of object detectors while the annotated datasets for object detection are hard to scale up due to the high labor cost.
The Novel Adaptive Fractional Order Gradient Decent Algorithms Design via Robust Control
The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex.
Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs
To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan.
Deep Feature Selection for Anomaly Detection Based on Pretrained Network and Gaussian Discriminative Analysis
Deep learning neural network serves as a powerful tool for visual anomaly detection (AD) and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain.
Ranked #53 on
Anomaly Detection
on MVTec AD
Neural Observer with Lyapunov Stability Guarantee for Uncertain Nonlinear Systems
In this paper, we propose a novel nonlinear observer based on neural networks, called neural observer, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems.
Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask.
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Semantic decoupled representation learning for remote sensing image change detection
Here, we propose a semantic decoupled representation learning for RS image CD.
Realtime CNN-based Keypoint Detector with Sobel Filter and CNN-based Descriptor Trained with Keypoint Candidates
We design Gaussian loss for the training process of SobelNet to detect corner points as keypoints.
