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Found 18 papers, 4 papers with code
Cross-Lingual Transfer with MAML on Trees
In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
+1
How does BERT process disfluency?
Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2).
Breeze-7B Technical Report
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Chinese.
Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite
In an effort to advance the evaluation of language models in Traditional Chinese and stimulate further research in this field, we have open-sourced our benchmark and opened the model for trial.
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data.
Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning
In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt.
Image generation with shortest path diffusion
We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring.
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese.
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
Recently, successful applications of reinforcement learning to chip placement have emerged.
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization.
Uniform Generalization Bounds for Overparameterized Neural Networks
As a byproduct of our results, we show the equivalence between the RKHS corresponding to the NT kernel and its counterpart corresponding to the Mat\'ern family of kernels, showing the NT kernels induce a very general class of models.
Optimal Order Simple Regret for Gaussian Process Bandits
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.
Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging
In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators.
How to distribute data across tasks for meta-learning?
We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks.
Meta-Learning with MAML on Trees
We show that TreeMAML improves the state of the art results for cross-lingual Natural Language Inference.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
+2
Model agnostic meta-learning on trees
In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related, and sharing information between unrelated tasks might hurt performance.
Optimal allocation of data across training tasks in meta-learning
They are tested on benchmarks with a fixed number of data-points for each training task, and this number is usually arbitrary, for example, 5 instances per class in few-shot classification.
Cyclic orthogonal convolutions for long-range integration of features
We propose a novel architecture that allows flexible information flow between features $z$ and locations $(x, y)$ across the entire image with a small number of layers.
