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Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks…

Computation and Language · Computer Science 2022-03-31 Danny Merkx , Stefan L. Frank

We motivate and formally define a new task for fine-tuning rule-like generalization in large language models. It is conjectured that the shortcomings of current LLMs are due to a lack of ability to generalize. It has been argued that,…

Computation and Language · Computer Science 2023-06-21 Paola Merlo

End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition. However, RNNs often struggle to…

Computer Vision and Pattern Recognition · Computer Science 2018-07-24 Ankush Gupta , Andrea Vedaldi , Andrew Zisserman

Validating Large Language Models with ReLM explores the application of formal languages to evaluate and control Large Language Models (LLMs) for memorization, bias, and zero-shot performance. Current approaches for evaluating these types…

Computation and Language · Computer Science 2025-04-18 Reece Adamson , Erin Song

While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to…

Computation and Language · Computer Science 2019-04-09 Nelson F. Liu , Omer Levy , Roy Schwartz , Chenhao Tan , Noah A. Smith

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation,…

Machine Learning · Computer Science 2023-11-02 Giuseppe Alessio D'Inverno , Simone Brugiapaglia , Mirco Ravanelli

Right-linear (or left-linear) grammars are a well-known class of context-free grammars computing just the regular languages. They may naturally be written as expressions with (least) fixed points but with products restricted to letters as…

Logic in Computer Science · Computer Science 2024-01-25 Anupam Das , Abhishek De

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In…

Neural and Evolutionary Computing · Computer Science 2015-02-20 Wojciech Zaremba , Ilya Sutskever , Oriol Vinyals

Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectiveness of GNNs. To mitigate this challenge, Test-Time Training…

Machine Learning · Computer Science 2024-04-23 Jiaxin Zhang , Yiqi Wang , Xihong Yang , Siwei Wang , Yu Feng , Yu Shi , Ruicaho Ren , En Zhu , Xinwang Liu

Algorithmic trading requires short-term tactical decisions consistent with long-term financial objectives. Reinforcement Learning (RL) has been applied to such problems, but adoption is limited by myopic behaviour and opaque policies. Large…

Machine Learning · Computer Science 2025-10-28 Adam Darmanin , Vince Vella

Large language models (LLMs) have received increasing attention. However, due to the complexity of its capabilities, how to rationally evaluate the capabilities of LLMs is still a task to be solved. We propose the RoCar method, which…

Computation and Language · Computer Science 2024-11-12 Ming Wang , Wenfang Wu , Chongyun Gao , Daling Wang , Shi Feng , Yifei Zhang

Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video…

Machine Learning · Computer Science 2015-10-20 Zachary C. Lipton , John Berkowitz , Charles Elkan

The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or…

Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple…

Computation and Language · Computer Science 2021-04-13 Atul Sahay , Ayush Maheshwari , Ritesh Kumar , Ganesh Ramakrishnan , Manjesh Kumar Hanawal , Kavi Arya

Probabilistic B\"uchi automata are a natural generalization of PFA to infinite words, but have been studied in-depth only rather recently and many interesting questions are still open. PBA are known to accept, in general, a class of…

Formal Languages and Automata Theory · Computer Science 2020-04-30 Christof Löding , Anton Pirogov

Language models, being at the heart of many NLP problems, are always of great interest to researchers. Neural language models come with the advantage of distributed representations and long range contexts. With its particular dynamics that…

Neural and Evolutionary Computing · Computer Science 2018-11-19 Thomas Cherian , Akshay Badola , Vineet Padmanabhan

Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This…

Machine Learning · Computer Science 2022-04-20 Gang Chen , Yu Lu , Rong Su , Zhaodan Kong

Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the…

Robotics · Computer Science 2022-09-19 Zhenshan Bing , Alexander Koch , Xiangtong Yao , Kai Huang , Alois Knoll

Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs…

Machine Learning · Computer Science 2019-01-31 Valentin Khrulkov , Oleksii Hrinchuk , Ivan Oseledets

Recurrent neural networks (RNNs) are known to be universal approximators of dynamic systems under fairly mild and general assumptions. However, RNNs usually suffer from the issues of vanishing and exploding gradients in standard RNN…

Signal Processing · Electrical Eng. & Systems 2024-10-28 Shashank Jere , Lizhong Zheng , Karim Said , Lingjia Liu