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This paper introduces PermuFormer, an autoregressive transformer pretrained on multi-task, multi-encoding data for permutation-focused tasks in algebraic combinatorics, demonstrating effective fine-tuning on downstream tasks compared to baselines.
Hapi is a U-Net Swin Transformer that provides medium-range hydrological forecasts at continental scale, outperforming physics-based and AI models in flood detection across the contiguous United States.
AstroForge is developing an autonomous AI model named 'Solo' to control its spacecraft, reducing the need for ground operators. The company plans to test this technology on a mission in 2027.
The paper introduces the Modality Discrepancy Transformer (MDT), a novel multimodal fusion framework that enhances cross-modal discrepancy modeling for recognizing ambivalence and hesitancy in clinical videos, outperforming baselines on the BAH dataset.
This paper proposes a panoramic aerodynamic performance prediction framework for turbomachinery using a transformer-enhanced neural operator (TNO), which first predicts basic physical quantities like temperature and pressure before estimating key performance parameters, significantly reducing computational cost while maintaining accuracy.
The paper introduces FlowTSFM, an encoder-based time series foundation model that interprets depth as recurrent quantile transport, achieving competitive accuracy with fewer parameters compared to baselines like Chronos-2.
This article promotes the PyTorch Conference North America, highlighting a session by Witold Czubala from UBS on using PyTorch-based transformer models for time-to-event prediction in wealth management from longitudinal email data.
The paper proposes an integrated framework for multiclass gait disorder classification using bilateral GRF and COP signals, with 3D visualization and explainable AI, achieving high accuracy.
The paper presents C3T, a thread-structured temporal model for predicting sentiment shifts in social media conversation trees using counterfactual causal reasoning, and introduces the CaSiRe dataset for causal sentiment reasoning.
Trained a small transformer model from scratch to achieve 44% accuracy on the ARC-AGI-1 benchmark for only 67 cents, demonstrating improvements in speed, accuracy, and cost-effectiveness over previous methods.
This paper proposes using AstroPT, a transformer trained on galaxy images, as a testbed for studying concept emergence during training, finding that galaxy properties emerge in a fixed difficulty-based sequence, which can inform mechanistic interpretability methods for large language models.
This paper presents a two-stage transformer framework for predicting the risk and timing of acute exacerbations of COPD using raw waveform data from home ventilators, demonstrating improved performance over baseline models.
This paper investigates using in-context learning with the TabPFN model to predict perceived neighborhood walkability based on built environment features for mobility-impaired older adults, achieving higher performance than baseline models and employing SHAP-IQ for interaction analysis.