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.
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
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).
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.
In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
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.
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.
First, the label co-occurrence graph is obtained according to the statistical information of the data set.
%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
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.
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.
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)