Playing the Game of 2048
25 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Genetic Algorithm-based Polar Code Construction for the AWGN Channel
We propose a new polar code construction framework (i. e., selecting the frozen bit positions) for the additive white Gaussian noise (AWGN) channel, tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding.
Decoder-tailored Polar Code Design Using the Genetic Algorithm
We propose a new framework for constructing polar codes (i. e., selecting the frozen bit positions) for arbitrary channels, and tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding.
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data.
MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning
Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks (DNNs) on computer clusters.
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.
Faster Person Re-Identification
In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy.
Playing 2048 With Reinforcement Learning
The game of 2048 is a highly addictive game.
Optimistic Temporal Difference Learning for 2048
Our experiments show that both TD and TC learning with OI significantly improve the performance.
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research.
Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models
In this work, we perform a systematic study of this accuracy vs. efficiency trade-off on two widely used long-sequence models - Longformer-Encoder-Decoder (LED) and Big Bird - during fine-tuning and inference on four datasets from the SCROLLS benchmark.