In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
Example Output: Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

Example Input: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four families of problems for which some of the commonly used existing algorithms fail or suffer significant difficulty. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.
Example Output: Failures of Deep Learning

Example Input: The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, rangeor bearing-only observations relative to a set of fixed beacons provide an effective means of estimating a robot’s location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are spatially distributed in the environment, and how the robot’s location is inferred based on noisy measurements of range or bearing. However, it is computationally challenging to search for a placement and an inference strategy that, together, are optimal. Existing methods decouple these decisions, forgoing optimality for tractability. We propose a new optimization approach to jointly determine the beacon placement and inference algorithm. We model inference as a neural network and incorporate beacon placement as a differentiable neural layer. This formulation allows us to optimize placement and inference by jointly training the inference network and beacon layer. We evaluate our method on different localization problems and demonstrate performance that exceeds hand-crafted baselines.
Example Output:
Jointly Optimizing Placement and Inference for Beacon-based Localization