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Found 47 papers, 24 papers with code
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination.
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
CNN-based SNNs are the current mainstream of neuromorphic computing.
Forecasting Cryptocurrency Staking Rewards
This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors.
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding
The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged.
AlignBench: Benchmarking Chinese Alignment of Large Language Models
We will provide public APIs for evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs' Chinese alignment.
ClusVPR: Efficient Visual Place Recognition with Clustering-based Weighted Transformer
VPR is particularly difficult due to the presence of duplicate regions and the lack of attention to small objects in complex scenes, resulting in recognition deviations.
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection
In this paper, we for the first time explore helpful multi-modal contextual knowledge to understand novel categories for open-vocabulary object detection (OVD).
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
BLIVA demonstrates significant capability in decoding real-world images, irrespective of text presence.
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
Deceptive AI Ecosystems: The Case of ChatGPT
ChatGPT, an AI chatbot, has gained popularity for its capability in generating human-like responses.
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM).
Multi-modal Queried Object Detection in the Wild
To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed.
Ranked #1 on
Few-Shot Object Detection
on ODinW-35
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.
Equity Promotion in Public Transportation
Specifically, we aim to design a strategy of allocating a given limited budget to different candidate programs such that the overall social equity is maximized, which is defined as the minimum covering ratio among all pre-specified protected groups of households (based on race, income, etc.).
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (OCNLI_50K)
SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL).
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations.
PyTSK: A Python Toolbox for TSK Fuzzy Systems
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems.
RecipeSnap -- a lightweight image-to-recipe model
Recipes from Recipe1M dataset and corresponding recipe embeddings are collected as a recipe library, which are used for image encoder training and image query later.
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics
The current work aims to understand two basic questions regarding the deep neural network (DNN) method: what data the DNN needs and how general the DNN method can be.
Equity Promotion in Online Resource Allocation
We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government.
Fairness Maximization among Offline Agents in Online-Matching Markets
Examples of online and offline agents include keywords (online) and sponsors (offline) in Google Advertising; workers (online) and tasks (offline) in Amazon Mechanical Turk (AMT); riders (online) and drivers (offline when restricted to a short time window) in ridesharing.
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue.
Ranked #11 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models
In this paper, we detail the relationship between convolutions and self-attention in natural language tasks.
Towards Corruption-Agnostic Robust Domain Adaptation
However, due to unpredictable corruptions (e. g., noise and blur) in real data like web images, domain adaptation methods are increasingly required to be corruption robust on target domains.
Pose Recognition with Cascade Transformers
Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.
Co-Scale Conv-Attentional Image Transformers
In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms.
Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions
We show that two defuzzification operations, LogTSK and HTSK, the latter of which is first proposed in this paper, can avoid the saturation.
Line Segment Detection Using Transformers without Edges
In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free.
Ranked #1 on
Line Segment Detection
on York Urban Dataset
(FH metric)
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
Constellation Nets for Few-Shot Learning
The success of deep convolutional neural networks builds on top of the learning of effective convolution operations, capturing a hierarchy of structured features via filtering, activation, and pooling.
Ranked #15 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
A Unified Model for the Two-stage Offline-then-Online Resource Allocation
In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework.
Trading the System Efficiency for the Income Equality of Drivers in Rideshare
Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit.
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
Deep implicit field regression methods are effective for 3D reconstruction from single-view images.
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints
We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F).
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly.
Guided Variational Autoencoder for Disentanglement Learning
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.
Neural Program Synthesis By Self-Learning
Neural inductive program synthesis is a task generating instructions that can produce desired outputs from given inputs.
Rethinking Exposure Bias In Language Modeling
Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned on its previous predictions unseen from the training corpus.
EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training
A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly.
Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos
Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF.
LISA: a MATLAB package for Longitudinal Image Sequence Analysis
Given two contrasting sequences of images, the image processing in LISA starts with selecting a region of interest in two representative images, followed by automatic or manual segmentation and registration.
A NUMA-Aware Provably-Efficient Task-Parallel Platform Based on the Work-First Principle
By abiding by the work-first principle, we are able to obtain a platform that is work efficient, provides the same theoretical guarantees as the classic work stealing scheduler, and mitigates work inflation.
Distributed, Parallel, and Cluster Computing Performance
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds.
Ranked #6 on
3D Part Segmentation
on IntrA
