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Found 58 papers, 19 papers with code
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs).
ZV-Sim: Probabilistic Simulation Framework for Pre-emergent Novel Zoonose Tracking
ZV-Sim is an open-source, modular Python framework for probabilistic simulation and analysis of pre-emergent novel zoonotic diseases using pervasive sensing data.
Preliminary Characterization of Bio-inspired Dog-Nose Sampler for Aerosol Detection
Before aerosols can be sensed, sampling technologies must capture the particulate matter of interest.
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
In this paper, we introduce PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels.
Leveraging Audio Representations for Vibration-Based Crowd Monitoring in Stadiums
In this paper, we present ViLA (Vibration Leverage Audio), a vibration-based method that reduces the dependency on labeled data by pre-training with unlabeled cross-modality data.
WeVibe: Weight Change Estimation Through Audio-Induced Shelf Vibrations In Autonomous Stores
In response to these limitations, we made the following contributions: (1) We propose WeVibe, a first item weight change estimation system through active shelf vibration sensing.
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Previous models for voice interactions are categorized as native and aligned.
A Separable Self-attention Inspired by the State Space Model for Computer Vision
Mamba is an efficient State Space Model (SSM) with linear computational complexity.
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibitive token sequences.
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #7 on
on GPQA
ZhoBLiMP: a Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese
Whether and how language models (LMs) acquire the syntax of natural languages has been widely evaluated under the minimal pair paradigm.
Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation
LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability.
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training data available in various domains.
Hierarchical learning control for autonomous robots inspired by central nervous system
Mammals can generate autonomous behaviors in various complex environments through the coordination and interaction of activities at different levels of their central nervous system.
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions
Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Neuromorphic Imaging with Super-Resolution
Neuromorphic imaging is an emerging technique that imitates the human retina to sense variations in dynamic scenes.
AnyTrans: Translate AnyText in the Image with Large Scale Models
This paper introduces AnyTrans, an all-encompassing framework for the task-Translate AnyText in the Image (TATI), which includes multilingual text translation and text fusion within images.
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2, 000 GPUs on Perlmutter and 16, 000 GPUs on Frontier.
Vim-F: Visual State Space Model Benefiting from Learning in the Frequency Domain
In recent years, State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences such as language understanding.
Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
Real-world data deviating from the independent and identically distributed (i. i. d.)
One-Step Late Fusion Multi-view Clustering with Compressed Subspace
Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance.
A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
In this paper, we propose CMS-PRL, a pre-training reinforcement learning method inspired by the Central Motor System (CMS).
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Transferring a molecular foundation model for polymer property predictions
Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and materials discovery.
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising
Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result.
Neuromorphic Imaging and Classification with Graph Learning
Bio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams.
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models
Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities of large language models (LLMs).
Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging
Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range.
Characterization and Generation of 3D Realistic Geological Particles with Metaball Descriptor based on X-Ray Computed Tomography
This algorithm can capture the main contour of parental particles with a series of non-overlapping spheres and refine surface-texture details through gradient search.
PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration
In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
The lack of competency awareness makes NMT untrustworthy.
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range.
Sharp Attention for Sequence to Sequence Learning
Attention mechanism has been widely applied to tasks that output some sequence from an input image.
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs.
A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient
Then, a global optimal feature set (the channel width, the flow velocity, the channel slope and the cross sectional area) was proposed through numerical comparison of the distilled local optimums in performance with representative ML models.
A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams
Whereas implicit ML-driven methods are black-boxes in nature, explicit ML-driven methods have more potential in prediction of LDC.
Context-Interactive Pre-Training for Document Machine Translation
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
iii) The partition level information has not been utilized in existing work.
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Multi-view clustering is an important yet challenging task in machine learning and data mining community.
Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings
One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise.
Quantum Physics
Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation
In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.
Extending Label Smoothing Regularization with Self-Knowledge Distillation
The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed.
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
Lattice Transformer for Speech Translation
Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
PSA: A novel optimization algorithm based on survival rules of porcellio scaber
Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies.
