Search Results for author:
Found 14 papers, 12 papers with code
Mixtral of Experts
In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
Ranked #11 on
Question Answering
on PIQA
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
Ranked #4 on
Zero-Shot Video Question Answer
on NExT-GQA
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #3 on
Few-Shot Learning
on MedConceptsQA
HyperTree Proof Search for Neural Theorem Proving
With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy.
Ranked #1 on
Automated Theorem Proving
on Metamath set.mm
(Pass@32 metric)
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we present a memory-augmented approach for image-goal navigation.
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.
Addressing Some Limitations of Transformers with Feedback Memory
Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks.
TorchBeast: A PyTorch Platform for Distributed RL
TorchBeast is a platform for reinforcement learning (RL) research in PyTorch.
Efficient keyword spotting using dilated convolutions and gating
We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states.
Spoken Language Understanding on the Edge
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications.
Ranked #4 on
Spoken Language Understanding
on Snips-SmartSpeaker
Federated Learning for Keyword Spotting
We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors.
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
Ranked #47 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
