Search Results for author:
Found 10 papers, 8 papers with code
Surprisal Predicts Code-Switching in Chinese-English Bilingual Text
We also found sentence length to be a significant predictor, which has been related to sentence complexity.
Enabling On-Device LLMs Personalization with Smartphone Sensing
To the best of our knowledge, this is the first framework to provide on-device LLMs personalization with smartphone sensing.
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift.
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO).
Learning To Learn and Remember Super Long Multi-Domain Task Sequence
We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting.
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
In this paper, we explore a more practical and challenging setting where task distribution changes over time with domain shift.
Transformer-based Conditional Variational Autoencoder for Controllable Story Generation
In this paper, we advocate to revive latent variable modeling, essentially the power of representation learning, in the era of Transformers to enhance controllability without hurting state-of-the-art generation effectiveness.
Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events
Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story".
Implicit Deep Latent Variable Models for Text Generation
Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation.
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Technological breakthroughs allow us to collect data with increasing spatio-temporal resolution from complex interaction systems.
