Neural Information Processing Systems http://nips.cc/

Point processes are widely used statistical tools for modeling event occurrence patterns within continuous spatio-temporal domains.

Despite the attention that the problem of path planning for tethered robots has garnered in the past few decades, the approaches proposed to solve it typically rely on a discrete representation of the configuration space and do not exploit a model that can simultaneously capture the topological information of the tether and the continuous location of the robot. In this work, we explicitly build a topological model of the configuration space of a tethered robot starting from a polygonal representation of the workspace where the robot moves. To do so, we first establish a link between the configuration space of the tethered robot and the universal covering space of the workspace, and then we exploit this link to develop an algorithm to compute a simplicial complex model of the configuration space. We show how this approach improves the performances of existing algorithms that build other types of representations of the configuration space. The proposed model can be computed in a fraction of the time required to build traditional homotopy-augmented graphs, and is continuous, allowing to solve the path planning task for tethered robots using a broad set of path planning algorithms.

The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering.

Neural Information Processing Systems http://nips.cc/

Point processes are widely used statistical tools for modeling event occurrence patterns within continuous spatio-temporal domains.

We thank all the reviewers for carefully reading of the manuscript and constructive comments. Reviewer #1: Assumptions A.2 and A.3 used in Algorithm 2. Reviewer #3: There seem to be several misunderstandings regarding the steps of our algorithm and its analysis. To maintain the list constant, for every added point another point is removed.

Neural Information Processing Systems http://nips.cc/

Recent state-of-the-art authorship attribution methods learn authorship representations of texts in a latent, non-interpretable space, hindering their usability in real-world applications. Our work proposes a novel approach to interpreting these learned embeddings by identifying representative points in the latent space and utilizing LLMs to generate informative natural language descriptions of the writing style of each point. We evaluate the alignment of our interpretable space with the latent one and find that it achieves the best prediction agreement compared to other baselines. Additionally, we conduct a human evaluation to assess the quality of these style descriptions, validating their utility as explanations for the latent space. Finally, we investigate whether human performance on the challenging AA task improves when aided by our system's explanations, finding an average improvement of around +20% in accuracy.