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Found 35 papers, 4 papers with code
Language Resource Efficient Learning for Captioning
Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources.
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64. 8 and 156. 5, respectively.
Scaling Laws For Dense Retrieval
In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.
Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
Our in-depth analysis of these logs reveals that user prompt reformulation is heavily dependent on the individual user's capability, resulting in significant variance in the quality of reformulation pairs.
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc.
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.
Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval
With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems.
Random resistive memory-based deep extreme point learning machine for unified visual processing
Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results
Canonical Correlation Analysis (CCA) has been widely applied to jointly embed multiple views of data in a maximally correlated latent space.
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
This paper attempts to highlight a use-case of state-of-the-art image segmentation models like SAM for crop-type mapping and related specific needs of the agriculture industry, offering a potential avenue for automatic, efficient, and cost-effective data products for precision agriculture practices.
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank.
SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements.
Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure
A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably.
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank
This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task.
A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis
High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model.
An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis
Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i. e., the Particle Swarm Optimization (PSO)-LFA model.
An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors
The A2BAS algorithm consists of two sub-algorithms.
A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications.
Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need.
Deep Fraud Detection on Non-attributed Graph
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation
First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions.
Dynamic Network Embedding Survey
Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk
Node embeddings have been attracting increasing attention during the past years.
Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation
Finally, the proposed hybrid loss in a four hierarchy-pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries.
Better Captioning with Sequence-Level Exploration
In this work, we show the limitation of the current sequence-level learning objective for captioning tasks from both theory and empirical result.
Improving Captioning for Low-Resource Languages by Cycle Consistency
Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets
Under certain conditions, dPCA is proved to be least-squares optimal in recovering the component vector unique to the target data relative to background data.
Canonical Correlation Analysis of Datasets with a Common Source Graph
Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not hidden sources are commonly present in two (or more) datasets.
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets
Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction.
Generating Video Descriptions with Topic Guidance
In addition to predefined topics, i. e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model.
Video Captioning with Guidance of Multimodal Latent Topics
For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos.
Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification
In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications.
