A5050872351- Natural Language Processing Techniques
- Topic Modeling
- Multimodal Machine Learning Applications
- Ethics and Social Impacts of AI
- Machine Learning in Materials Science
- Speech and dialogue systems
- Software Engineering Research
- Advanced Graph Neural Networks
- Advanced Text Analysis Techniques
- Reinforcement Learning in Robotics
- Multi-Agent Systems and Negotiation
- Data Quality and Management
- Distributed Sensor Networks and Detection Algorithms
- Adversarial Robustness in Machine Learning
- Software System Performance and Reliability
- Neural Networks and Applications
University of Oxford
2023
IBM (United States)
2016-2018
Several large cloze-style context-questionanswer datasets have been introduced recently: the CNN and Daily Mail news data Children's Book Test.Thanks to size of these datasets, associated text comprehension task is well suited for deep-learning techniques that currently seem outperform all alternative approaches.We present a new, simple model uses attention directly pick answer from context as opposed computing using blended representation words in document usual similar models.This makes...
Many papers have been published on the knowledge base completion task in past few years. Most of these introduce novel architectures for relation learning that are evaluated standard datasets like FB15k and WN18. This paper shows accuracy almost all models can be outperformed by an appropriately tuned baseline — our reimplementation DistMult model. Our findings cast doubt claim performance improvements recent due to architectural changes as opposed hyper-parameter tuning or different...
There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article making case for the community move larger and as step that direction it proposing BookTest, new dataset similar popular Children's Book Test (CBT), however more than 60 times larger. We show training improves accuracy our Attention-Sum Reader model original CBT test by much margin...
If autonomous AI systems are to be reliably safe in novel situations, they will need incorporate general principles guiding them recognize and avoid harmful behaviours. Such may supported by a binding system of regulation, which would the underlying widely accepted. They should also specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil role such serve as foundation both an international regulatory building...
Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This large part due to rapidly growing market demand for capable of behaviour. Due business process nature these conversations, end-to-end machine learning systems are generally not viable option, as generated must be deployable verifiable on behalf businesses authoring them. In this work, we...
Dialogue systems have many applications such as customer support or question answering. Typically they been limited to shallow single turn interactions. However more advanced career coaching planning a trip require much complex multi-turn dialogue. Current limitations of conversational made it difficult that personalization, customization and context dependent We tackle this challenging problem by using domain-independent AI automatically create dialogue plans, customized guide towards...
The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using set demonstrations from an expert optimizing for unknown to the learner. resulting rewards can then be used synthesize apprentice policy that performs well on same or similar task. A key challenge in IRL bridging computational gap between hypothesis space possible and likelihood, often defined terms Q values: vanilla needs solve costly forward planning problem - going...
If autonomous AI systems are to be reliably safe in novel situations, they will need incorporate general principles guiding them recognize and avoid harmful behaviours. Such may supported by a binding system of regulation, which would the underlying widely accepted. They should also specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil role such serve as foundation both an international regulatory building...
As AI systems become increasingly autonomous, aligning their decision-making to human preferences is essential. In domains like autonomous driving or robotics, it impossible write down the reward function representing these by hand. Inverse reinforcement learning (IRL) offers a promising approach infer unknown from demonstrations. However, obtaining demonstrations can be costly. Active IRL addresses this challenge strategically selecting most informative scenarios for demonstration, reducing...
Many papers have been published on the knowledge base completion task in past few years. Most of these introduce novel architectures for relation learning that are evaluated standard datasets such as FB15k and WN18. This paper shows accuracy almost all models can be outperformed by an appropriately tuned baseline - our reimplementation DistMult model. Our findings cast doubt claim performance improvements recent due to architectural changes opposed hyper-parameter tuning or different...
We point out important problems with the common practice of using best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time is trained, one gets different result due to random factors in training process, which include parameter initialization data shuffling. Reporting does not appropriately address this stochasticity. normalized expected best-out-of-$n$ ($\text{Boo}_n$) as way correct problems.
Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data Children's Book Test. Thanks to size of these datasets, associated text comprehension task is well suited for deep-learning techniques that currently seem outperform all alternative approaches. We present a new, simple model uses attention directly pick answer from context as opposed computing using blended representation words in document usual similar models. This...
If autonomous AI systems are to be reliably safe in novel situations, they will need incorporate general principles guiding them recognize and avoid harmful behaviours. Such may supported by a binding system of regulation, which would the underlying widely accepted. They should also specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil role such serve as foundation both an international regulatory building...