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Found 19 papers, 3 papers with code
Rapid and Precise Topological Comparison with Merge Tree Neural Networks
To address this challenge, we introduce the merge tree neural networks (MTNN), a learned neural network model designed for merge tree comparison.
Derivative-free tree optimization for complex systems
Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts.
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions.
A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss
Low-light image enhancement aims to improve the perception of images collected in dim environments and provide high-quality data support for image recognition tasks.
Visualizing Topological Importance: A Class-Driven Approach
This paper presents the first approach to visualize the importance of topological features that define classes of data.
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.
Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies
The current framework for climate change negotiation models presents several limitations that warrant further research and development.
Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies
We demonstrate our negotiation model within the RICE-N framework, illustrating a promising approach for facilitating international cooperation on climate change mitigation.
Mesoscopic Collective Activity in Excitatory Neural Fields: Cross-frequency Coupling
Because the brain is a multi-scale system too, a similar mechanism must be active in the brain.
Mesoscopic Collective Activity in Excitatory Neural Fields: Governing Equations
An ensemble average over a cell population then produces a closed system of equations involving two mesoscopic state variables: the density of kinetic energy J, carried by sodium ionic currents, and the excitability H of the neural field, which could be described as the average state of gating variable h. The resulting model is represented as essentially a subthreshold process; and the dynamical role of the firing rate is naturally reassessed as describing energy transfers.
Deconfounded Visual Grounding
We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck.
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting
Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e. g. persons) in images.
A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering
In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances.
Comparing Distance Metrics on Vectorized Persistence Summaries
Different vectorizations of PD summary are commonly used in machine learning applications, however distances between vectorized persistence summaries may differ greatly from the distances between the original PDs.
Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation
Then, the interpolated signals are used together with the high-quality tissue microstructure computed from the source dataset to train deep networks that perform tissue microstructure estimation for the target dataset.
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues
Prior research has shown that textual information in a firm{'}s financial statement can be used to predict its stock{'}s risk level.
Look Back and Predict Forward in Image Captioning
Most existing attention-based methods on image captioning focus on the current word and visual information in one time step and generate the next word, without considering the visual and linguistic coherence.
Attend More Times for Image Captioning
Most attention-based image captioning models attend to the image once per word.
