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Found 23 papers, 8 papers with code
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters.
Circuit Transformer: End-to-end Circuit Design by Predicting the Next Gate
Then, can circuits also be mastered by a a sufficiently large "circuit model", which can conquer electronic design tasks by simply predicting the next logic gate?
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
VideoAssembler: Identity-Consistent Video Generation with Reference Entities using Diffusion Model
Identity-consistent video generation seeks to synthesize videos that are guided by both textual prompts and reference images of entities.
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Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks
In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning.
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
In this way, temporal consistency can be kept with video LDM while high-fidelity from the image LDM can also be exploited.
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Conditioned on an initial video clip with a small number of frames, additional frames are iteratively generated by reusing the original latent features and following the previous diffusion process.
DocDiff: Document Enhancement via Residual Diffusion Models
Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks.
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery
In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation.
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle.
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection
As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis.
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation
Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model.
On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
Nonlinear ICA Using Volume-Preserving Transformations
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
VideoLT: Large-scale Long-tailed Video Recognition
In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.
Hierarchical Neural Architecture Search via Operator Clustering
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.
An Improvement of PAA on Trend-Based Approximation for Time Series
In this paper, we propose two new approaches for time series that utilize approximate trend feature information.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Non-local NetVLAD Encoding for Video Classification
This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.
Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning
Our work represents a new application of automated planning on an emerging and challenging machine learning paradigm.
Social Computing for Mobile Big Data in Wireless Networks
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain.
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.
Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation
Kurtosis of 1D projections provides important statistical characteristics of natural images.
