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Found 18 papers, 6 papers with code
Scaling Laws for a Multi-Agent Reinforcement Learning Model
A substantial amount of attention has been dedicated as a consequence to the description of scaling laws, although mostly for supervised learning and only to a reduced extent for reinforcement learning frameworks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Generic catastrophic poverty when selfish investors exploit a degradable common resource
Oligarchs are characterized by payoffs that are finite and not decreasing when $N$ increases.
Collective strategy condensation towards class-separated societies
In physics, the wavefunctions of bosonic particles collapse when the system undergoes a Bose-Einstein condensation.
Emotions as abstract evaluation criteria in biological and artificial intelligences
The resulting timeline of experienced emotions is compared with the `character' of the agent, which is defined in terms of a preferred distribution of emotional states.
Nonlinear Dendritic Coincidence Detection for Supervised Learning
Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field.
A devil's advocate view on 'self-organized' brain criticality
Stationarity of the constituents of the body and of its functionalities is a basic requirement for life, being equivalent to survival in first place.
When to end a lock down? How fast must vaccination campaigns proceed in order to keep health costs in check?
We propose a simple rule of thumb for countries which have embarked on a vaccination campaign while still facing the need to keep non-pharmaceutical interventions (NPI) in place because of the ongoing spread of SARS-CoV-2.
Incentives for accelerating the production of Covid-19 vaccines in the presence of adjustment costs
Delays in the availability of vaccines are costly as the pandemic continues.
Local homeostatic regulation of the spectral radius of echo-state networks
This result allows us to introduce two local homeostatic synaptic scaling mechanisms, termed flow control and variance control, that implicitly drive the spectral radius towards the desired value under working conditions.
Investment vs. reward in a competitive knapsack problem
Natural selection drives species to develop brains, with sizes that increase with the complexity of the tasks to be tackled.
The economics of stop-and-go epidemic control
We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates.
A generic framework for task selection driven by synthetic emotions
The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting the individual tasks.
Suppression of topological Mott-Hubbard phases by multiple charge orders in the honeycomb extended Hubbard model
We investigate the competition between charge-density-wave (CDW) states and a Coulomb interaction-driven topological Mott insulator (TMI) in the honeycomb extended Hubbard model.
Strongly Correlated Electrons
Emergent lattices with geometrical frustration in doped extended Hubbard models
For this purpose we investigate the phase diagram of doped extended Hubbard models on two lattices: (i) the honeycomb lattice with on-site $U$ and nearest-neighbor $V$ Coulomb interactions at $3/4$ filling ($n=3/2$) and (ii) the triangular lattice with on-site $U$, nearest-neighbor $V$, and next-nearest-neighbor $V'$ Coulomb interactions at $3/8$ filling ($n=3/4$).
Strongly Correlated Electrons
How to test for partially predictable chaos
For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set.
Chaotic Dynamics Disordered Systems and Neural Networks Computational Physics
Spontaneous symmetry breaking in correlated wave functions
We show that Jastrow-Slater wave functions, in which a density-density Jastrow factor is applied onto an uncorrelated fermionic state, may possess long-range order even when all symmetries are preserved in the wave function.
Strongly Correlated Electrons
Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
This self-limiting behavior allows for stable online learning for arbitrary durations.
Attractor Metadynamics in Adapting Neural Networks
We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics.
