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Found 13 papers, 1 papers with code
Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase.
Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps.
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance.
Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs).
RimiRec: Modeling Refined Multi-interest in Hierarchical Structure for Recommendation
Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items.
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs
Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations.
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively.
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario
Previous works usually assume the malware families are known to the classifier in a close-set scenario, i. e., testing families are the subset or at most identical to training families.
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
We argue that disregarding the connection between major and minor classes, i. e., correspond to the global and local information, respectively, is the cause of the problem.
Identifying critical nodes in complex networks by graph representation learning
Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science.
Convergence rate to the Tracy-Widom laws for the largest eigenvalue of Wigner matrices
We show that the fluctuations of the largest eigenvalue of a real symmetric or complex Hermitian Wigner matrix of size $N$ converge to the Tracy--Widom laws at a rate $O(N^{-1/3+\omega})$, as $N$ tends to infinity.
Probability Mathematical Physics Mathematical Physics 15B52, 60B20
CenterFace: Joint Face Detection and Alignment Using Face as Point
Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power.
Ranked #14 on
Face Detection
on WIDER Face (Hard)
