Search Results for author:
Found 9 papers, 1 papers with code
FedLPA: Personalized One-shot Federated Learning with Layer-Wise Posterior Aggregation
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
Can the Query-based Object Detector Be Designed with Fewer Stages?
Query-based object detectors have made significant advancements since the publication of DETR.
Semi-supervised Domain Adaptation with Inter and Intra-domain Mixing for Semantic Segmentation
Instead of solely using the scarce labeled data for supervision, we propose a novel SSDA framework that incorporates both inter-domain mixing and intra-domain mixing, where inter-domain mixing mitigates the source-target domain gap and intra-domain mixing enriches the available target domain information.
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Large-scale well-annotated datasets are of great importance for training an effective object detector.
A Survey on Personalized Affective Computing in Human-Machine Interaction
In this paper, we discuss the need for personalization in affective and personality computing (hereinafter referred to as affective computing).
TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation
However, finding a suitable model parallel schedule for an arbitrary neural network is a non-trivial task due to the exploding search space.
MapQA: A Dataset for Question Answering on Choropleth Maps
Our experimental results show that V-MODEQA has better overall performance and robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by capturing the unique properties in map question answering.
Harmonia: Near-Linear Scalability for Replicated Storage with In-Network Conflict Detection
Experimental results show that Harmonia improves the throughput of these protocols by up to 10X for a replication factor of 10, providing near-linear scalability up to the limit of our testbed.
Distributed, Parallel, and Cluster Computing
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares
We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.
