95 research outputs found
Bayesian Modeling of a Human MMORPG Player
This paper describes an application of Bayesian programming to the control of
an autonomous avatar in a multiplayer role-playing game (the example is based
on World of Warcraft). We model a particular task, which consists of choosing
what to do and to select which target in a situation where allies and foes are
present. We explain the model in Bayesian programming and show how we could
learn the conditional probabilities from data gathered during human-played
sessions.Comment: 30th international workshop on Bayesian Inference and Maximum
Entropy, Chamonix : France (2010
A Temporal Coherence Loss Function for Learning Unsupervised Acoustic Embeddings
AbstractWe train neural networks of varying depth with a loss function which imposes the output representations to have a temporal profile which looks like that of phonemes. We show that a simple loss function which maximizes the dissimilarity between near frames and long distance frames helps to construct a speech embedding that improves phoneme discriminability, both within and across speakers, even though the loss function only uses within speaker information. However, with too deep an architecture, this loss function yields overfitting, suggesting the need for more data and/or regularization
A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft
The task of keyhole (unobtrusive) plan recognition is central to adaptive
game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS)
game strategic (long term) planning. This paper presents a generic and simple
Bayesian model for RTS build tree prediction from noisy observations, which
parameters are learned from replays (game logs). This unsupervised machine
learning approach involves minimal work for the game developers as it leverage
players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it
yields high quality and robust predictions, that can feed an adaptive AI.Comment: 7 pages; Artificial Intelligence and Interactive Digital
Entertainment Conference (AIIDE 2011), Palo Alto : \'Etats-Unis (2011
Getting the most out of your tokenizer for pre-training and domain adaptation
Tokenization is an understudied and often neglected component of modern LLMs.
Most published works use a single tokenizer for all experiments, often borrowed
from another model, without performing ablations or analysis to optimize
tokenization. Moreover, the tokenizer is generally kept unchanged when
fine-tuning a base model. In this paper, we show that the size,
pre-tokenization regular expression, and training data of a tokenizer can
significantly impact the model's generation speed, effective context size,
memory usage, and downstream performance. We train specialized Byte-Pair
Encoding code tokenizers, and conduct extensive ablations on the impact of
tokenizer design on the performance of LLMs for code generation tasks such as
HumanEval and MBPP, and provide recommendations for tokenizer hyper-parameters
selection and switching the tokenizer in a pre-trained LLM. We perform our
experiments on models trained from scratch and from pre-trained models,
verifying their applicability to a wide range of use-cases. We find that when
fine-tuning on more than 50 billion tokens, we can specialize the tokenizer of
a pre-trained LLM to obtain large gains in generation speed and effective
context size
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