We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’ local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents.
In this paper, we propose Weak-Attention Suppression (WAS), a method that dynamically induces sparsity in attention probabilities. We demonstrate that WAS leads to consistent Word Error Rate (WER) improvement over strong transformer baselines.
In this paper we will give a retrospective of machine learning in compiler optimisation from its earliest inception, through some of the works that set themselves apart, to today’s deep learning, finishing with our vision of the field’s future. Index Terms—machine learning, compilers.
As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios. The goal in each scenario is to create a digital sketch using a simple web interface.
We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user’s day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs.
We train machine learning models to predict a subset of preferences from the rest. We find that preferences in the generative art form cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.
As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neurosymbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively.
We present a general computational approach that enables a machine to generate a dance for any input music. We encode intuitive, flexible heuristics for what a ‘good’ dance is: the structure of the dance should align with the structure of the music.