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Found 31 papers, 11 papers with code
Few-Shot Cross-System Anomaly Trace Classification for Microservice-based systems
Within the same MSS, our framework achieves an average accuracy of 93. 26\% and 85. 2\% across 50 meta-testing tasks for Trainticket and OnlineBoutique, respectively, when provided with 10 instances for each task.
SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction
In response to this challenge, we propose a novel diffusion model reconstruction framework tailored for 3D seismic data.
SportQA: A Benchmark for Sports Understanding in Large Language Models
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP).
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial.
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
To address this gap, our study undertakes a thorough evaluation of Gemini's performance in complex reasoning tasks that necessitate the integration of commonsense knowledge across modalities.
Dual Branch Network Towards Accurate Printed Mathematical Expression Recognition
Over the past years, Printed Mathematical Expression Recognition (PMER) has progressed rapidly.
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector
By integrating diverse datasets, log representation methods and anomaly detectors, LogLead facilitates comprehensive benchmarking in log analysis research.
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
This regularity, together with gradient descent using a large learning rate that favors flatter regions, results in these nontrivial dynamical behaviors.
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data.
TRAM: Benchmarking Temporal Reasoning for Large Language Models
In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the temporal reasoning capabilities of large language models (LLMs).
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera
Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data.
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Explicit object-level semantic correspondence between audio and visual modalities is established by gathering object information from visual features with predefined audio queries.
Metacognitive Prompting Improves Understanding in Large Language Models
This study underscores the potential to amplify the understanding abilities of LLMs and highlights the benefits of mirroring human introspective reasoning in NLU tasks.
An Empirical Study on the Robustness of the Segment Anything Model (SAM)
The Segment Anything Model (SAM) is a foundation model for general image segmentation.
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3. 5, GPT-4, and Bard, within the realm of clinical language understanding tasks.
DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality
First, we adopt Transformers instead of RNNs to process sequential data and design a relaxation representation for vector outlines, markedly improving the model's capability and stability of synthesizing long and complex outlines.
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning
As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU).
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
The problem of optimization on Stiefel manifold, i. e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied.
Enhancing Transformer Efficiency for Multivariate Time Series Classification
Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy.
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis
In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission.
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification
However, distilling knowledge from two teacher models could result in the student model making some redundant predictions.
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Moreover, we rigorously establish an implicit bias of GD induced by such a large learning rate, termed 'balancing', meaning that magnitudes of $X$ and $Y$ at the limit of GD iterations will be close even if their initialization is significantly unbalanced.
Empirical Quantitative Analysis of COVID-19 Forecasting Models
In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training.
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks.
Ranked #48 on
Semantic Segmentation
on ADE20K val
Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients
However, blindly pursuing complex classifiers is unwise as it also brings the risk of greater performance variation.
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
End-to-End Video Instance Segmentation with Transformers
Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem.
Ranked #33 on
Video Instance Segmentation
on YouTube-VIS validation
CenterMask: single shot instance segmentation with point representation
Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
