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Analyzing Information Flow in Transformers
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Date: 30th January 2020, 11:00 AM-12:00 PM
Speaker: Lena Voita , Yandex Research, University of Amsterdam
Abstract :
We will discuss what, how and why Transformers learn by analyzing
1. the mechanisms the model uses to encode different kinds of information;
2. how training objective defines information flow in a model.
First, we will start with an in-depth analysis of multi-head attention. Using attribution methods, we will assess the importance of individual heads and will show that the most important heads play interpretable roles. Surprisingly, all the rest of the heads are redundant and, using our novel heads-pruning method, can be pruned with almost no loss in translation quality.
Then, we will look at how the representations of individual tokens in the Transformer evolve between layers under different learning objectives: MT, LM and MLM (BERT-style). While previous work mostly used so-called ‘probing tasks’ and has made some interesting observations, an explanation of the process behind the observed behavior has been lacking. I will attempt to explain more generally why such behavior is observed by characterizing how the learning objective determines the information flow in the model. I look at this task from the information bottleneck perspective on learning in neural networks and will show that patterns in information flow are substantially different. For example, while LMs gradually forget past when forming predictions about future, for MLMs the evolution proceeds in two stages of context encoding and token reconstruction.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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