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Found 18 papers, 4 papers with code
Modularized Syntactic Neural Networks for Sentence Classification
In existing works, aggregating on a syntax tree usually considers local information of sub-trees.
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively.
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks.
Integration of cognitive tasks into artificial general intelligence test for large models
During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application.
Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling
The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario.
A Practical Two-stage Ranking Framework for Cross-market Recommendation
The proposed solution of our team WSDM_Coggle_ is selected as the second place submission.
Deep Auto-encoder with Neural Response
In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificial neurons.
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective
Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.
Self-supervised Models are Good Teaching Assistants for Vision Transformers
These findings motivate us to introduce an self-supervised teaching assistant (SSTA) besides the commonly used supervised teacher to improve the performance of transformers.
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their practical deployments on resource-limited devices.
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
Contrastive Learning for Compact Single Image Dehazing
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
Ranked #5 on
Image Dehazing
on RS-Haze
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the environment.
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment.
