Search Results for author:
Found 34 papers, 15 papers with code
A Hybrid System for NLPTEA-2020 CGED Shared Task
As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we present the latest model of the Gemini family, Gemini 1. 5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #19 on
Code Generation
on HumanEval
Insomnia impairs muscle function via regulating protein degradation and muscle clock
The grip strength reduced in SD group.
Efficient IoT Inference via Context-Awareness
While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i. e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments.
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention.
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods.
Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets.
Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems
However, the inherent intricacy and uncertainty in information dissemination pose significant challenges, and the ill-posed nature of the source localization problem further exacerbates these challenges.
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
The HarsanyiNet is designed on the theoretical foundation that the Shapley value can be reformulated as the redistribution of Harsanyi interactions encoded by the network.
UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner.
Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering
Introducing prior auxiliary information from the knowledge graph (KG) to assist the user-item graph can improve the comprehensive performance of the recommender system.
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion
In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input.
Human Activity Recognition in an Open World
Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples.
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
First, GLI is designed to incentivize \emph{dataset contributors}.
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model
In addition, to enhance diversity, we incorporate the idea of soft word substitution for data augmentation which replaces a word with a probabilistic distribution over the vocabulary.
Measuring Human Perception to Improve Open Set Recognition
The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms.
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario
Origin-Destination (OD) matrices record directional flow data between pairs of OD regions.
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset.
Series Photo Selection via Multi-view Graph Learning
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos.
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations.
City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds
A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing.
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased.
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP.
Efficient Second-Order Optimization for Deep Learning with Kernel Machines
However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training.
Regularized Sequential Latent Variable Models with Adversarial Neural Networks
The recurrent neural networks (RNN) with richly distributed internal states and flexible non-linear transition functions, have overtaken the dynamic Bayesian networks such as the hidden Markov models (HMMs) in the task of modeling highly structured sequential data.
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Representation learning tries to learn a common low dimensional space for the representations of users and items.
Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments.
Costly Features Classification using Monte Carlo Tree Search
We consider the problem of costly feature classification, where we sequentially select the subset of features to make a balance between the classification error and the feature cost.
Hierarchical Neural Architecture Search for Single Image Super-Resolution
To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search.
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training
A standard machine translation system is usually trained on parallel corpus composed of clean text and will perform poorly on text with recognition noise, a gap well known in speech translation community.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images.
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i. e. popularity and recency) on whether a RS item can be linked to a KB entity.
Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency
This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR).
