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Found 11 papers, 6 papers with code
GloNets: Globally Connected Neural Networks
Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks.
On the Behavior of the Payoff Amounts in Simple Interest Loans in Arbitrage-Free Markets
We assume that the payoff amounts are established contractually at time zero, whence the requirement that no arbitrage may arise this way The first goal of this paper is to study this new formula and derive it within a model of a loan market in which loans are bought and sold at simple interest, interest rates change over time, and no arbitrage opportunities exist.
Increasing biases can be more efficient than increasing weights
We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure.
Artificial intelligence and renegotiation of commercial lease contracts affected by pandemic-related contingencies from Covid-19. The project A.I.A.Co
This paper aims to investigate the possibility of using artificial intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of continuing execution contracts, or those with deferred or periodic execution, as well as, more generally, to deal with exceptional events and contingencies.
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?
In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome.
Curious Explorer: a provable exploration strategy in Policy Learning
These bounds can be used to prove PAC convergence and sample efficiency results when a PAC optimizer is plugged in Curious Explorer.
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state.
On locally conformally Kähler threefolds with algebraic dimension two
The paper is part of an attempt of understanding non-K\"ahler threefolds.
Differential Geometry Complex Variables 32Q57, 32J17, 53C55
Pseudo Random Number Generation: a Reinforcement Learning approach
In this context, N is the length of the period of the generated sequence, and the policy is iteratively improved using the average value of an appropriate test suite run over that period.
SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)
We develop a new model that can be applied to any perfect information two-player zero-sum game to target a high score, and thus a perfect play.
SAI, a Sensible Artificial Intelligence that plays Go
We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero paradigm.
