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Found 10 papers, 5 papers with code
Bits of Grass: Does GPT already know how to write like Whitman?
This study examines the ability of GPT-3. 5, GPT-3. 5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8192 tokens).
How good are variational autoencoders at transfer learning?
Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis.
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges
We tested our methodology on 52 jokes in a crowd of four AI voters with different humour types: affiliative, self-enhancing, aggressive and self-defeating.
FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders
We show that the discrepancies between the IDE of the mean and sampled representations of a VAE after only a few steps of training reveal the presence of passive variables in the latent space, which, in well-behaved VAEs, indicates a superfluous number of dimensions.
How do Variational Autoencoders Learn? Insights from Representational Similarity
The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications.
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
However, their mean representations, which are generally used for downstream tasks, have recently been shown to be more correlated than their sampled counterpart, on which disentanglement is usually measured.
SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes
This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition.
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers.
Reinforcement Learning using Augmented Neural Networks
Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to approximate complex mappings from state spaces to value functions.
Improving Language Modelling with Noise-contrastive estimation
In this paper, we showed that NCE can be a successful approach in neural language modelling when the hyperparameters of a neural network are tuned appropriately.
