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Found 10 papers, 1 papers with code
A Mathematical Theory for Learning Semantic Languages by Abstract Learners
Using density evolution analysis, we demonstrate the emergence of learned skills when the ratio of the size of training texts to the number of skills exceeds a certain threshold.
InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent
This research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark.
A Simple Explanation for the Phase Transition in Large Language Models with List Decoding
We show that there is a critical threshold such that the expected number of erroneous candidate sequences remains bounded when an LLM is below the threshold, and it grows exponentially when an LLM is above the threshold.
Constructions and Comparisons of Pooling Matrices for Pooled Testing of COVID-19
The family of PPoL matrices can dynamically adjust their column weights according to the prevalence rates and could be a better alternative than using a fixed pooling matrix.
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation
In addition to solving the network embedding problem, both proposed GCNs are capable of performing dimensionality reduction.
A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons
By relating the propagation probabilities in the IC model to the transmission rates and recovering rates in the SIR model, we show 2 approaches of social distancing that can lead to a reduction of $R_0$.
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem
For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of {\em dynamic blind rendezvous policies}, i. e., at the $t^{th}$ time slot each user selects channel $i$ independently with probability $p_i(t)$, $i=1, 2, \ldots, N$.
ETTR Bounds and Approximation Solutions of Blind Rendezvous Policies in Cognitive Radio Networks with Random Channel States
We first consider two channel models: (i) the fast time-varying channel model (where the channel states are assumed to be independent and identically distributed in each time slot), and (ii) the slow time-varying channel model (where the channel states remain unchanged over time).
Information Theory Information Theory
K-sets+: a Linear-time Clustering Algorithm for Data Points with a Sparse Similarity Measure
We then extend the applicability of the K-sets+ algorithm from data points in a semi-metric space to data points that only have a symmetric similarity measure.
A Mathematical Theory for Clustering in Metric Spaces
Such a duality result leads to a dual K-sets algorithm for clustering a set of data points with a cohesion measure.
