8 citations · 13 across the 13 of their papers we have counts for
13 papers
Interestingness as an Inductive Heuristic for Future Compression Progress
Vincent Herrmann, Jürgen Schmidhuber
One of the bottlenecks on the way towards recursively self-improving systems is the challenge of interestingness: the ability to prospectively identify which tasks or data hold the…
Multiple Token Divergence: Measuring and Steering In-Context Computation Density
Vincent Herrmann, Eric Alcaide, Michael Wand +1
Measuring the in-context computational effort of language models is a key challenge, as metrics like next-token loss fail to capture reasoning complexity. Prior methods based on la…
Measuring In-Context Computation Complexity via Hidden State Prediction
Vincent Herrmann, Róbert Csordás, Jürgen Schmidhuber
Detecting when a neural sequence model does "interesting" computation is an open problem. The next token prediction loss is a poor indicator: Low loss can stem from trivially predi…
Upside Down Reinforcement Learning with Policy Generators
Jacopo Di Ventura, Dylan R. Ashley, Vincent Herrmann +2
Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work,…
Automatic Album Sequencing
Vincent Herrmann, Dylan R. Ashley, Jürgen Schmidhuber
Album sequencing is a critical part of the album production process. Recently, a data-driven approach was proposed that sequences general collections of independent media by extrac…
Learning Useful Representations of Recurrent Neural Network Weight Matrices
Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber
Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights t…
Unsupervised Musical Object Discovery from Audio
Joonsu Gha, Vincent Herrmann, Benjamin Grewe +2
Current object-centric learning models such as the popular SlotAttention architecture allow for unsupervised visual scene decomposition. Our novel MusicSlots method adapts SlotAtte…
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge, Haozhe Liu, Francesco Faccio +23
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each o…
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks
Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber
There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given q…
On Narrative Information and the Distillation of Stories
Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad +1
The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to be the overlap in infor…
Goal-Conditioned Generators of Deep Policies
Francesco Faccio, Vincent Herrmann, Aditya Ramesh +2
Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies, given goals encoded in special command inputs. Here we study goal-conditioned neural nets (NNs) that…
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
Francesco Faccio, Aditya Ramesh, Vincent Herrmann +2
Learning to evaluate and improve policies is a core problem of Reinforcement Learning (RL). Traditional RL algorithms learn a value function defined for a single policy. A recently…
Automatic Embedding of Stories Into Collections of Independent Media
Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad +2
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections. To…