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Presenters often collect audience feedback through practice talks to refine their presentations. In formative interviews, we find that although text feedback and verbal discussions allow presenters to receive feedback, organizing that feedback into ...
Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a ...
When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task --- the task "features" --- as well as how to combine these features into a single ...
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco-...
Personal thermal comfort models aim to predict an individual's thermal comfort response, instead of the average response of a large group. Recently, machine learning algorithms have proven to be having enormous potential as a candidate for personal ...
Ubiquitous deployment of IoT sensors marks a defining characteristic of smart buildings, for they constitute the source of data on building operation, diagnosis, and maintenance. For machine learning applications in buildings, often the sensor data is ...
Federated learning (FL) is a machine learning paradigm that enables a cluster of decentralized edge devices to collaboratively train a shared machine learning model without exposing users' raw data. However, the intensive model training computation is ...
This paper presents a technique to interpret and visualize intermediate layers in generative CNNs trained on raw speech data in an unsupervised manner. We argue that averaging over feature maps after ReLU activation in each transpose convolutional layer ...
Making AI more trustworthy with a formal methods-based approach to AI system verification and validation.
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of paramount importance for the deployment of speech recognition systems that ...
As AI advances in capabilities and moves into the real world, its potential to benefit humanity seems limitless. Yet we see serious problems including racial and gender bias, manipulation by social media, and an arms race in lethal autonomous weapons. ...
Protein language models have enabled breakthrough approaches to protein structure prediction, function annotation, and drug discovery. A primary limitation to the widespread adoption of these powerful models is the high computational cost associated ...
Deep learning has achieved significant success in multimedia fields involving computer vision, natural language processing, and acoustics. However, research in adversarial learning also shows that they are highly vulnerable to adversarial examples. ...
Real-world visual recognition is far more complex than object recognition; there is stuff without distinctive shape or appearance, and the same object appearing in different contexts calls for different actions. While we need context-aware visual ...
Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in self-driving. However, cameras tend to fail in bad driving conditions, ...
While deep neural networks (DNNs) have shown to be successful in several domains like computer vision, non-DNN models such as linear models and gradient boosting trees are still considered state-of-the-art over tabular data. When using these models, ...
Artificial agents trained by deep reinforcement learning will likely encounter novel situations after deployment that were never seen during training. Our agent must be robust to handle such situations well. However, if we cannot rely on the average ...
Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors. We show that this is an unavoidable trade-off ...
As machine learning and data science applications grow ever more prevalent, there is an increased focus on data sharing and open data initiatives, particularly in the context of the African continent. Many argue that data sharing can support research ...
Government agencies are embracing machine learning to support a variety of resource allocation decisions. The U.S. Environmental Protection Agency (EPA), for example, has engaged academic research labs to test the use of machine learning in support of ...