TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality- versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose “fidelity-weighted learning” (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.

SOLUTION: We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data.

PROBLEM: To understand how object vision develops in infancy and childhood, it will be necessary to develop testable computational models. Deep neural networks (DNNs) have proven valuable as models of adult vision, but it is not yet clear if they have any value as models of development. As a first model, we measured learning in a DNN designed to mimic the architecture and representational geometry of the visual system (CORnet). We quantified the development of explicit object representations at each level of this network through training by freezing the convolutional layers and training an additional linear decoding layer. We evaluate decoding accuracy on the whole ImageNet validation set, and also for individual visual classes. CORnet, however, uses supervised training and because infants have only extremely impoverished access to labels they must instead learn in an unsupervised manner. We therefore also measured learning in a state-of-the-art unsupervised network (DeepCluster). CORnet and DeepCluster differ in both supervision and in the convolutional networks at their heart, thus to isolate the effect of supervision, we ran a control experiment in which we trained the convolutional network from DeepCluster (an AlexNet variant) in a supervised manner. We make predictions on how learning should develop across brain regions in infants. In all three networks, we also tested for a relationship in the order in which infants and machines acquire visual classes, and found only evidence for a counter-intuitive relationship. We discuss the potential reasons for this.

SOLUTION: Unsupervised networks learn from bottom up; machines and infants acquire visual classes in different orders

PROBLEM: Reinforcement learning and evolutionary algorithms can be used to create sophisticated control solutions. Unfortunately explaining how these solutions work can be difficult to due to their "black box" nature. In addition, the time-extended nature of control algorithms often prevent direct applications of explainability techniques used for standard supervised learning algorithms. This paper attempts to address explainability of blackbox control algorithms through six different techniques: 1) Bayesian rule lists, 2) Function analysis, 3) Single time step integrated gradients, 4) Grammar-based decision trees, 5) Sensitivity analysis combined with temporal modeling with LSTMs, and 6) Explanation templates. These techniques are tested on a simple 2d domain, where a simulated rover attempts to navigate through obstacles to reach a goal. For control, this rover uses an evolved multi-layer perception that maps an 8d field of obstacle and goal sensors to an action determining where it should go in the next time step. Results show that some simple insights in explaining the neural network are possible, but that good explanations are difficult.

SOLUTION:
Describes a series of explainability techniques applied to a simple neural network controller used for navigation.