Search Results for author:
Found 9 papers, 7 papers with code
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
Question Answering
on OBQA
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
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.
Mixtral of Experts
In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
Ranked #9 on
Question Answering
on PIQA
Canonical Tensor Decomposition for Knowledge Base Completion
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.
Ranked #3 on
Link Prediction
on FB15k
Tensor Decompositions for temporal knowledge base completion
Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.
Ranked #1 on
Link Prediction
on YAGO15k
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.
Ranked #3 on
Automated Theorem Proving
on miniF2F-valid
(Pass@100 metric)
Projected Canonical Decomposition for Knowledge Base Completion
However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.
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)
