# Mathematical Reasoning

10 papers with code • 0 benchmarks • 0 datasets

## Benchmarks

These leaderboards are used to track progress in Mathematical Reasoning
## Most implemented papers

# Analysing Mathematical Reasoning Abilities of Neural Models

The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.

# Compositional Generalization with Tree Stack Memory Units

We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.

# Measuring Mathematical Problem Solving With the MATH Dataset

To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.

# Training Verifiers to Solve Math Word Problems

State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.

# Learning to Prove Theorems via Interacting with Proof Assistants

Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.

# DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE).

# Reverse Operation based Data Augmentation for Solving Math Word Problems

Automatically solving math word problems is a critical task in the field of natural language processing.

# Compositional Processing Emerges in Neural Networks Solving Math Problems

A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition.

# Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning

Recent years have seen impressive performance of transformer-based models on different natural language processing tasks.

# A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis

We convert the DeepMind Mathematics Dataset into a reinforcement learning environment by interpreting it as a program synthesis problem.