CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
Attention is all you need
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
Hallucinations are inevitable on an infinite set of inputs but can be made statistically negligible with sufficient training data quality and quantity.
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
GlucoNet applies feature decomposition and knowledge distillation to a transformer model to forecast blood glucose levels from irregular multimodal data, reporting accuracy gains and model compression on data from 12 T1D participants.
citing papers explorer
-
CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder
CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
-
Hallucinations are inevitable but can be made statistically negligible
Hallucinations are inevitable on an infinite set of inputs but can be made statistically negligible with sufficient training data quality and quantity.
-
Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.
-
Large Language Model-Brained GUI Agents: A Survey
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
-
From System 1 to System 2: A Survey of Reasoning Large Language Models
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
-
Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
GlucoNet applies feature decomposition and knowledge distillation to a transformer model to forecast blood glucose levels from irregular multimodal data, reporting accuracy gains and model compression on data from 12 T1D participants.