We introduce an extension to the CLRS algorithmic learning benchmark,
pr...
We break the linear link between the layer size and its inference cost b...
Eye movements can reveal valuable insights into various aspects of human...
Graph Neural Networks (GNNs) have emerged as a powerful tool for learnin...
Music datasets play a crucial role in advancing research in machine lear...
Implicit Neural Representations (INRs) have emerged as a promising metho...
This paper proposes a set of simple adaptations to transform standard
me...
In September 2022, Ethereum transitioned from Proof-of-Work (PoW) to
Pro...
The transaction ordering dependency of the smart contracts building
dece...
We conduct a preliminary inquiry into the ability of generative transfor...
With Ethereum's transition from Proof-of-Work to Proof-of-Stake in Septe...
This paper presents a plug-and-play approach for translation with termin...
A group of n agents with numerical preferences for each other are to be
...
Traditional blockchain design gives miners or validators full control ov...
We provide a novel approach to construct generative models for graphs.
I...
While Artificial Intelligence (AI) models have achieved human or even
su...
The use of well-disentangled representations offers many advantages for
...
An assembly of n voters needs to decide on t independent binary issues.
...
Decentralized autonomous organizations (DAOs) are a recent innovation in...
In this study, we validate the findings of previously published papers,
...
Many classical blockchains are known to have an embarrassingly low
trans...
Anxiety levels in the AAVE community spiked in November 2022 as Avi Eise...
Federated Reinforcement Learning (FedRL) encourages distributed agents t...
Classical graph algorithms work well for combinatorial problems that can...
While rigid origami has shown potential in a large diversity of engineer...
Recent work has demonstrated that pre-trained language models (PLMs) are...
We propose a novel, fully explainable neural approach to synthesis of
co...
Denoising diffusion probabilistic models and score matching models have
...
Grammatical inference is a classical problem in computational learning t...
The learning of the simplest possible computational pattern – periodicit...
Integer sequences are of central importance to the modeling of concepts
...
Within just four years, the blockchain-based Decentralized Finance (DeFi...
Owing to their versatility, graph structures admit representations of
in...
Financial markets have evolved over centuries, and exchanges have conver...
A distributed directory is an overlay data structure on a graph G that
h...
We present a novel graph neural network we call AgentNet, which is desig...
This work proposes a novel proof-of-work blockchain incentive scheme suc...
The collection of eye gaze information provides a window into many criti...
Most Graph Neural Networks (GNNs) cannot distinguish some graphs or inde...
We propose the fully explainable Decision Tree Graph Neural Network (DT+...
This paper studies asynchronous message passing (AMP), a new paradigm fo...
We introduce two-crossing elections as a generalization of single-crossi...
Combinatorial auctions (CAs) allow bidders to express complex preference...
We approach the graph generation problem from a spectral perspective by ...
We empirically study the state of three prominent DAO governance systems...
User transactions on Ethereum's peer-to-peer network are at risk of bein...
Decentralized exchanges are revolutionizing finance. With their ever-gro...
We investigate the problem of Min-cost Perfect Matching with Delays (MPM...
Predatory trading bots lurking in Ethereum's mempool present invisible
t...
Digital money can be implemented efficiently by avoiding consensus. Howe...