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Found 49 papers, 21 papers with code
Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood.
SVD-Based Graph Fractional Fourier Transform on Directed Graphs and Its Application
Graph fractional Fourier transform (GFRFT) is an extension of graph Fourier transform (GFT) that provides an additional fractional analysis tool for graph signal processing (GSP) by generalizing temporal-vertex domain Fourier analysis to fractional orders.
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey
Remote sensing image super-resolution (RSISR) is a crucial task in remote sensing image processing, aiming to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts.
STRICT: Stress Test of Rendering Images Containing Text
In this paper, we introduce $\textbf{STRICT}$, a benchmark designed to systematically stress-test the ability of diffusion models to render coherent and instruction-aligned text in images.
Deep Learning and Explainable AI: New Pathways to Genetic Insights
Furthermore, we establish theoretical underpinnings to elucidate the origins of these constraints through formal mathematical demonstrations, aiming to assist genetic researchers in better understanding and designing models in the future.
VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism.
Physics-Trained Neural Network as Inverse Problem Solver for Potential Fields: An Example of Downward Continuation between Arbitrary Surfaces
Downward continuation is a critical task in potential field processing, including gravity and magnetic fields, which aims to transfer data from one observation surface to another that is closer to the source of the field.
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting
To address this issue, we introduce a novel approach called Semantic-CD, specifically designed for semantic change detection in remote sensing images.
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms.
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
This limits the model to understand the rich semantic information in the data and its reasoning ability for complex downstream tasks, while also lacking interpretability.
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign prediction.
Self-Explainable Graph Transformer for Link Sign Prediction
Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks.
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language.
Applications of Explainable artificial intelligence in Earth system science
In recent years, artificial intelligence (AI) rapidly accelerated its influence and is expected to promote the development of Earth system science (ESS) if properly harnessed.
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.
VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images.
MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector
The Mini-ASPP and PSA are specifically designed for shadow feature enhancement, thereby enabling the expression of local details and small objects.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models 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 #1 on
Zero-Shot Video Question Answer
on Zero-shot Video Question Answering on LongVideoBench
(using extra training data)
Navigating Decision Landscapes: The Impact of Principals on Decision-Making Dynamics
We explored decision-making dynamics in social systems, referencing the 'herd behavior' from prior studies where individuals follow preceding choices without understanding the underlying reasons.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Visual Question Answering (VQA)
on InfographicVQA
Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models.
Enhancing Signed Graph Neural Networks through Curriculum-Based Training
The prevailing training approach feeds samples (edges) to models in a random order, resulting in equal contributions from each sample during the training process, but fails to account for varying learning difficulties based on the graph's structure.
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations
Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations.
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL).
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning
Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required.
AI For Global Climate Cooperation 2023 Competition Proceedings
On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively.
Normalization Enhances Generalization in Visual Reinforcement Learning
Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce.
Dehazing-NeRF: Neural Radiance Fields from Hazy Images
Our method simulates the physical imaging process of hazy images using an atmospheric scattering model, and jointly learns the atmospheric scattering model and a clean NeRF model for both image dehazing and novel view synthesis.
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem
In this paper, we utilize the mathematical theory of isometric approximation to show an equivalence between the Triplet Loss sampled by hard negative mining and an optimization problem that minimizes a Hausdorff-like distance between the neural network and its ideal counterpart function.
READ: Large-Scale Neural Scene Rendering for Autonomous Driving
In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes.
Hybrid Reasoning Network for Video-based Commonsense Captioning
Firstly, we develop multi-commonsense learning for semantic-level reasoning by jointly training different commonsense types in a unified network, which encourages the interaction between the clues of multiple commonsense descriptions, event-wise captions and videos.
Content-Augmented Feature Pyramid Network with Light Linear Spatial Transformers for Object Detection
As one of the prevalent components, Feature Pyramid Network (FPN) is widely used in current object detection models for improving multi-scale object detection performance.
Minimal Cycle Representatives in Persistent Homology using Linear Programming: an Empirical Study with User's Guide
Cycle representatives of persistent homology classes can be used to provide descriptions of topological features in data.
Rademacher complexity of noisy quantum circuits
Noise in quantum systems is a major obstacle to implementing many quantum algorithms on large quantum circuits.
Quantum Physics
Effects of quantum resources on the statistical complexity of quantum circuits
We investigate how the addition of quantum resources changes the statistical complexity of quantum circuits by utilizing the framework of quantum resource theories.
On the statistical complexity of quantum circuits
In this work, we apply a particular measure of statistical complexity, namely the Rademacher complexity, to the quantum circuit model in quantum computation and study how the statistical complexity depends on various quantum circuit parameters.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.
Robust Gaussian Process Regression Based on Iterative Trimming
The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers.
OCNLI: Original Chinese Natural Language Inference
In this paper, we present the first large-scale NLI dataset (consisting of ~56, 000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI).
Organ size increases with obesity and correlates with cancer risk
Obesity increases significantly cancer risk in various organs.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.
Chinese Named Entity Recognition
named-entity-recognition
+2
How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning
Automatic dialogue evaluation plays a crucial role in open-domain dialogue research.
Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network
To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results.
MRS-VPR: a multi-resolution sampling based global visual place recognition method
We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching.
A Multi-Domain Feature Learning Method for Visual Place Recognition
Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically.
Observation data pre-processing and scientific data products generation of POLAR
This pipeline has been successfully applied for the use by the POLAR Science Data Center in IHEP after POLAR was launched and switched on.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
Ranked #6 on
Conversational Response Selection
on RRS
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
