625 citations · 2k across the 18 of their papers we have counts for
20 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Sandhini Agarwal +124
We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…
Considering Length Diversity in Retrieval-Augmented Summarization
Juseon-Do, Jaesung Hwang, Jingun Kwon +2
This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. W…
Predicting Ordinary Differential Equations with Transformers
Sören Becker, Michal Klein, Alexander Neitz +2
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observatio…
GPT-4 Technical Report
OpenAI, Josh Achiam, Steven Adler +278
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-wor…
Discovering ordinary differential equations that govern time-series
Sören Becker, Michal Klein, Alexander Neitz +2
Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and s…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Explanatory Learning: Beyond Empiricism in Neural Networks
Antonio Norelli, Giorgio Mariani, Luca Moschella +4
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomou…
Neural Symbolic Regression that Scales
Luca Biggio, Tommaso Bendinelli, Alexander Neitz +2
Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditio…
Learning explanations that are hard to vary
Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto +2
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning. We show that averaging gradients across examples -- akin to a…
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Giambattista Parascandolo, Lars Buesing, Josh Merel +6
Standard planners for sequential decision making (including Monte Carlo planning, tree search, dynamic programming, etc.) are constrained by an implicit sequential planning assumpt…
Generalization in anti-causal learning
Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf
The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i…
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Alexander Neitz, Giambattista Parascandolo, Stefan Bauer +1
We introduce a method which enables a recurrent dynamics model to be temporally abstract. Our approach, which we call Adaptive Skip Intervals (ASI), is based on the observation tha…
Tempered Adversarial Networks
Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou +1
Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanatio…
Learning Independent Causal Mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla +1
Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeli…
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä +2
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detect…
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo +3
Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend…
Convolutional Recurrent Neural Networks for Bird Audio Detection
EmreÇakır, Sharath Adavanne, Giambattista Parascandolo +2
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose…
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Emre Çakır, Giambattista Parascandolo, Toni Heittola +2
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are…
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (R…