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Found 17 papers, 7 papers with code
Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics
We found that probabilities represented by LMs were more likely to align with human judgments of being "tricked" by the NPI illusion which examines a structural dependency, compared to the comparative and the depth-charge illusions which require sophisticated semantic understanding.
Membrane Potential Batch Normalization for Spiking Neural Networks
All these BNs are suggested to be used after the convolution layer as usually doing in CNNs.
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration
We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD).
ReactGenie: An Object-Oriented State Abstraction for Complex Multimodal Interactions Using Large Language Models
We evaluated the accuracy of the language parser using elicited commands from crowd workers and evaluated the usability of the generated multimodal app with 16 participants.
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
Adaptive Data Augmentation for Contrastive Learning
In computer vision, contrastive learning is the most advanced unsupervised learning framework.
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out
Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA's understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA.
Representing Affect Information in Word Embeddings
A growing body of research in natural language processing (NLP) and natural language understanding (NLU) is investigating human-like knowledge learned or encoded in the word embeddings from large language models.
Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification
First, the label co-occurrence graph is obtained according to the statistical information of the data set.
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
Ranked #2 on
Metric Learning
on DyML-Animal
OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems.
Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
Modeling the US-China trade conflict: a utility theory approach
This paper models the US-China trade conflict and attempts to analyze the (optimal) strategic choices.
A Real-time Contribution Measurement Method for Participants in Federated Learning
However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
