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Found 26 papers, 11 papers with code
End-to-End Learnable Item Tokenization for Generative Recommendation
In order to achieve mutual enhancement between the two components, we propose a recommendation-oriented alignment approach by devising two specific optimization objectives: sequence-item alignment and preference-semantic alignment.
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
Based on these discrete codes, we enhance the collaborative information of contrastive views by considering neighborhood structure and semantic relevance respectively.
Enhanced Cascade Prostate Cancer Classifier in mp-MRI Utilizing Recall Feedback Adaptive Loss and Prior Knowledge-Based Feature Extraction
This method adjusts the training dynamically based on accuracy and recall in the validation set, resulting in high accuracy and recall simultaneously in the testing set. Thirdly, we design an Enhanced Cascade Prostate Cancer Classifier that classifies prostate cancer into different levels in an interpretable way, which refines the classification results and helps with clinical intervention.
HSFusion: A high-level vision task-driven infrared and visible image fusion network via semantic and geometric domain transformation
Specifically, to minimize the gap between semantic and geometric representation, we design two separate domain transformation branches by CycleGAN framework, and each includes two processes: the forward segmentation process and the reverse reconstruction process.
LLMBox: A Comprehensive Library for Large Language Models
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs.
Distributed Rule Vectors is A Key Mechanism in Large Language Models' In-Context Learning
We further show that the rule vectors encode a high-level abstraction of rules extracted from the demonstrations.
Diffusion Models Are Innate One-Step Generators
To address this problem, instance-based distillation methods have been proposed to distill a one-step generator from a DM by having a simpler student model mimic a more complex teacher model.
Ranked #2 on
Image Generation
on ImageNet 64x64
Waveform Design for Joint Communication and SAR Imaging Under Random Signaling
Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios.
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors.
Parallel Algorithms for Exact Enumeration of Deep Neural Network Activation Regions
Our work has three main contributions: (1) we present a novel algorithm framework and parallel algorithms for region enumeration; (2) we implement one of our algorithms on a variety of network architectures and experimentally show how the number of regions dictates runtime; and (3) we show, using our algorithm's output, how the dimension of a region's affine transformation impacts further partitioning of the region by deeper layers.
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
A Locality-based Neural Solver for Optical Motion Capture
Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking.
Towards Head Computed Tomography Image Reconstruction Standardization with Deep Learning Assisted Automatic Detection
Three-dimensional (3D) reconstruction of head Computed Tomography (CT) images elucidates the intricate spatial relationships of tissue structures, thereby assisting in accurate diagnosis.
Reciprocal Sequential Recommendation
Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties.
Rethinking Population-assisted Off-policy Reinforcement Learning
In this paper, we first analyze the use of off-policy RL algorithms in combination with population-based algorithms, showing that the use of population data could introduce an overlooked error and harm performance.
Preserving Locality in Vision Transformers for Class Incremental Learning
In this paper, we encourage the model to preserve more local information as the training procedure goes on and devise a Locality-Preserved Attention (LPA) layer to emphasize the importance of local features.
A holistically 3D-printed flexible millimeter-wave Doppler radar: Towards fully printed high-frequency multilayer flexible hybrid electronics systems
Flexible hybrid electronics (FHE) is an emerging technology enabled through the integration of advanced semiconductor devices and 3D printing technology.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor
Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.
Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces
Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices.
A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices.
A Topological Nomenclature for 3D Shape Analysis in Connectomics
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
Technical report on Conversational Question Answering
Conversational Question Answering is a challenging task since it requires understanding of conversational history.
Multifunctional Metasurface Design with a Generative Adversarial Network
Metasurfaces have enabled precise electromagnetic wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices.
A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks
Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices.
Generating Sentences Using a Dynamic Canvas
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language.
