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Found 10 papers, 7 papers with code
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #1 on
Question Answering
on MedQA
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
Synergistic Graph Fusion via Encoder Embedding
In this paper, we introduce a novel approach called graph fusion embedding, designed for multi-graph embedding with shared vertex sets.
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends.
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models
Binary code summarization, while invaluable for understanding code semantics, is challenging due to its labor-intensive nature.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
Learning without gradient descent encoded by the dynamics of a neurobiological model
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work.
