Can we build machines able to learn and work seamlessly with humans? How do machines, humans and animals learn from each other, and can we improve on these processes and implement them in new domains?

The learning algorithms mostly use some mechanisms or assumptions by either putting some restrictions on the space of hypotheses or can be said as the underlying model space. This mechanism is known ...

Recently, two researchers from the University of Montreal, Yoshua Bengio and Anirudh Goyal proposed new inductive biases that are meant to boost the deep learning performance. This paper focuses ...

This work presents a novel systematic methodology to analyse the capabilities and limitations of Large Language Models (LLMs) with feedback from a formal inference engine, on logic theory induction.

This is our implementation for the following paper: Jiaren Xiao, Quanyu Dai, Xiaochen Xie, James Lam, and Ka-Wai Kwok. "Adversarially regularized graph attention networks for inductive learning on ...

In Kornell and Bjork (2008) reported a study that investigated the effect of spacing on inductive learning, i.e., learning a new category by observing different instances from that category. In ...

Inductive learning requires abstracting concepts and categories from examples, that is, learning to generalize examples. The paper by Kornell et al. (2010) examined the influence of spacing or ...

Much of the world’s population use computers for everyday tasks, but most fail to benefit from the power of computation due to their inability to program. Most crucially, users often have to perform ...