ICLR 2020

Variational Template Machine for Data-to-Text Generation

  • A graphical model for generating text $y$ from structured data $x$
  • Similar to a variational autoencoder, but adds latent variables that represent a template $z$ and content $c$
  • Continuous latent variables generate diverse output
  • Reconstruction loss for output given data and template
  • Template preserving loss: template variable can reconstruct the text
The graphical model of a Variational Template Machine. Ye et al.

Variational Inference

  • Approximation for Bayesian inference with graphical models
  • Computing posterior probabilities is hard because of the integration required to compute the evidence probability
  • Approximates the posterior distribution using a variational posterior from a family of distributions
  • Computing the KL divergence between the approximate and exact posterior would be equally hard, since it depends on the evidence probability
  • Minimizing KL divergence is equal to minimizing the negative evidence lower bound (ELBO)
  • ELBO is log evidence subtracted from the KL divergence
  • The optimization problem is not as hard as integration

Meta-Learning with Warped Gradient Descent

  • Model consists of task layers and warp layers
  • Task layers are updated using a normal loss
  • Meta-learn warp-layers that modify the loss surface to be smoother
  • Novelty: non-linear warp layers avoid dependence on the trajectory or the initialization, depending only on the task parameter updates at the current position of the search space

Meta-Learning

  • Learning some parameters of the optimizer such as initialization
  • Optimize for the final model accuracy as a function of the initialization
  • When using gradient descent, the objective is differentiable
  • Backpropagate through all training steps back into the initialization
  • Vanishing / exploding gradients when the loss surface is very flat or steep
  • Costly—usually scales to a handful of training steps only

Transformer-XH

  • Adaptation of Transformer for structured text
  • For example, multi-evidence reasoning: answer questions, following words that are linked to Wikipedia
  • Extra hop attention attends to the first token of another sequence (representative of the entire sequence)

Deep Double Descent

  • Bigger model means lower training loss
  • At some point test error starts to increase, but with large enough models decreases again
  • Occurs across various architectures (CNNs, ResNets, Transformers), data domains (NLP, vision), and optimizer (SGD, Adam)
  • Also occurs when increasing training time
  • In some cases increasing training data can hurt performance
  • Effective model complexity takes training time into account

Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization

  • Unstable mean and variance estimation with too small batch sizes
  • Batch renormalization (BRN): corrects batch mean and variance by moving average
  • Moving average batch normalization: moving average of variance mean reduction + weight centralization

Compressive Transformer for Long-Range Sequence Modelling

  • Train a language model on segments similar to Transformer-XL
  • When moving to the next segment, the oldest N activations in the memory are compressed using a compressing function
  • Lightweight and Dynamic Convolution: depth-wise separable convolution that runs in linear time
  • Transformer-XL: train a language model on segments, but include activations from the previous segment in “extended context”
  • Sparse Transformer: sparse attention masks
  • Adaptive Attention Span: different attention heads can have longer or shorter spans of attention

A Closer Look at Deep Policy Gradients

  • Policy gradient methods: optimize policy parameters to maximize the expected reward
  • Variance reduction using baseline—separate the quality of the action from the quality of the state
  • A canonical choice of baseline function is the value function
  • Surrogate objective is a simplification of reward maximization used by modern policy gradient algorithms (policy is divided by the old policy, Schulman et al. 2015)
  • Measure of gradient variance: mean pairwise correlation (similarity) between gradient samples
  • Visualization of optimization landscapes with different number of samples per estimate

Laurent Dinh: Invertible Models and Normalizing Flows

  • A model finds a representation $h = f(x)$ of a datapoint $x$ (in practice, an image)
  • Generative models (VAE, GAN) can produce an image $x$ from its representation $h$
  • Normalizing flow is a sequence of invertible transformations
  • Reversible generative models can encode an image into a latent space, making it possible to interpolate between two images

Variational Autoencoder (VAE)

  • A standard autoencoder learns representations with distinct clusters in the latent space
  • VAE encoder produces a set of means and variances, and then samples the inputs of the decoder
  • The latent space is continuous
  • Only approximate inference of latent variables from a datapoint
Training an autoencoder on MNIST results in distinct clusters. Hinton and Salakhutdinov.

Generative Adversarial Network (GAN)

  • A generator network creates fake images and a discriminator network learns to distinguish them from real images
  • Images cannot be encoded into the latent space

NICE

  • Finds a representation such that $p(h)$ factorizes as $\prod p(h_d)$, where $h_d$ are independent latent variables (arguably a “good” representation)
  • Prior distribution $p(h_d)$ is Gaussian or logistic
  • Training: maximize the likelihood of the data using a “change of variables” formula
  • $f(x)$ has to be invertible—achieved by splitting $x$ into $x_1$ and $x_2$ and using a transformation that transforms them into $x_1$ and $x_2 + m(x_1)$ respectively
  • Sampling images: sample from $p(h)$ and use the inverse of $f(x)$

A Probabilistic Formulation of Unsupervised Text Style Transfer

  • Change text style, keeping the semantic meaning unchanged
  • Machine translation, sentiment transfer (positive ↔ negative)
  • Unsupervised (only a nonparallel corpus)
  • Previous work on text style transfer: autoencoding loss + adversarial loss using a language model as a discriminator
  • Previous work on machine translation: cycle structures for unsupervised machine translation
  • Novelty: probabilistic formulation of the above heuristic training strategies
  • Translations of language A are “latent sequences” of language B and vice versa
  • We want to utilize a language model prior within each language
  • Train using variational inference
A graphical model for text style transfer. He et al.

Estimating Gradients for Discrete Random Variables by Sampling without Replacement

  • Strategies for obtaining gradients for discrete outputs: smoothening the outputs (relaxation), or sampling (REINFORCE)
  • REINFORCE: move the gradient inside the expectation and estimate it using a sample
  • With multiple samples one can use the average as a baseline
  • Sampling without replacement can be done efficiently by taking the top $k$ of Gumbel variables
  • Probability for sampling an unordered sample can be calculated as a sum over all possible permutations
  • The estimator is changed to work with an unordered set

Reformer

  • Memory efficiency: reversible residual layers, chunked FF and attention layers
  • Time complexity: attention within buckets created using locality sensitive hashing

A Theoretical Analysis of the Number of Shots in Few-Shot Learning

  • Prototypical networks cannot handle different numbers of shots between classes
  • Performance drops when there’s a mismatch in the number of shots between meta-training and testing
  • Trade-off between minimizing intra-class variance and maximizing inter-class variance is different when clustering a different number of embeddings
  • Proposes an embedding space transformation

Prototypical Networks

  • Aggregate experiences from learning other tasks to learn a few-shot task
  • Form prototypes of each class as the average embedding of the labeled “support” examples
  • Class likelihoods from the distances from the embedding of the current example to the prototypes
Prototypical networks in the few-shot (left) and zero-shot (right) scenarios. Snell et al.

Mixed Precision DNNs

  • Fixed-point representation for the weights and activations, with a different bitwidth for each layer
  • A quantizer DNN is learned using gradient-based methods
  • Which parameters (bitwidth, step size, minimum value, maximum value) to use for parameterization of uniform and power-of-two quantizations?
  • The gradients with regard to the quantizer parameters are bounded and decoupled when choosing step size and maximum value, or minimum value and maximum value
  • How to learn the parameters?
  • A penalty term is added to the loss to enforce size constraints for the weights and activations

Training Binary Neural Networks with Real-to-Binary Convolutions

  • Binary convolution can be implemented using fast xnor and pop-count operations
  • Per-channel scaling is used to produce real-valued outputs
  • Teacher-student with a real-valued teacher

On Mutual Information Maximization for Representation Learning

  • Unsupervised learning based on information theoretic concepts
  • InfoMax principle: a good representation should have high mutual information with the input
  • MMI alone is not sufficient for representation learning, but modern methods work well in practice
  • Multi-view approach: maximize mutual information between different views of the same input
  • For example: split an image in half, encode both parts independently, and compare the mutual information between the parts
  • If the representation encodes high-level features of the image, mutual information will be high; if it encodes noise, mutual information will be low

A Mutual Information Maximization Perspective of Language Representation Learning

  • Many language tasks can be formulated as maximizing an objective function that is a lower bound on mutual information between different parts of the text sequence
  • BERT (masked LM): word and corrupted word context, or sentence and following sentence
  • Skip-gram (word2vec): word and word context
  • InfoWorld (proposed by the authors): sentence and n-gram, both encoded using Transformer

Mutual Information Neural Estimation

  • Mutual information: the amount the uncertainty about $X$ is reduced by knowing the value of $Z$
  • Maximizing mutual information directly is infeasible
  • The mutual information between $X$ and $Z$ can be expressed as the KL divergence between the joint probability distribution and the product of the marginal distributions: $I(X;Z) = D_{KL}(P_{XZ} \parallel P_X P_Z)$
  • $E_P[f(x)] - \log E_Q[e^{f(x)}]$, where $f(x)$ is any real-valued function for which the expectations are finite, is always less than or equal to the KL divergence between $P$ and $Q$
  • Donsker-Varadhan representation for KL divergence: supremum of this lower bound is equal to the KL divergence
  • In theory, any function can be represented with a neural network ⇒ we can train a neural network $f(x)$ to maximize the lower bound

InfoNCE

  • Maximize mutual information between target $x$ and context $c$
  • One positive sample from $p(x \mid c)$ and $N-1$ negative samples from $p(x)$
  • Loss based on noise contrastive estimation

ALBERT

  • Small “sandwich” layer reduce the number of embedding parameters
  • By default shares all parameters between layers
  • Additional next-sentence prediction loss
  • Dropout removed
  • More data

Incorporating BERT into Neural Machine Translation

  • Initialize weights of NMT encoder from BERT (degradation)
  • Initialize encoder and decoder weights with cross-lingual BERT trained on a multilingual corpus (small improvement)
  • Create embeddings using BERT (significant improvement)
  • BERT-fused NMT: additional attention to BERT (whose parameters are fixed) in each layer
  • Drop-net trick: with certain probability perform a regularization step—use only BERT-encoder attention or self-attention
  • SOTA results in semi-supervised NMT

Mixout

  • Weight decay towards previous model parameters prevents catastrophic forgetting on the pretrained task
  • Mixout sets parameters from a randomly selected neuron to those of the pretrained model during fine-tuning
  • Corresponds to adaptive weight decay towards the pretrained model

Network Deconvolution

  • There is a lot of correlation between nearby pixels, even when an image is not blurred
  • Correlation in data causes gradient descent to take more steps
  • Correlation between dimensions can be removed with a coordinate transform
  • Calculate the correlation at every layer and apply inverse filtering
  • Results in a sparse representation

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

  • By repeatedly training and pruning connections, model size can be reduced
  • Even randomly initialized networks can be pruned prior to training, based on connection sensitivity
  • It is unclear why pruning the initialization is effective
  • Scaling of the initialization can have a critical impact

Monotonic Multihead Attention

  • Simultaneous translation: start translating before reading the full input
  • Monotonic attention: stepwise probability for decision whether to read a source token or write a target token
  • State of the art: Monotonic Infinite Loopback Attention (based on LSTM)
  • Novelty: Transformer with multihead monotonic attention
  • Independent stepwise probabilities for different heads
  • A source token is read if the fastest head decides to read
  • A target token is written if all the heads finish reading
  • Implemented in Fairseq

Revisiting Self-Training for Neural Sequence Generation

  • Self-training: train a teacher model using labeled data and a student model using the predictions of the teacher on unlabeled data
  • Fine-tune the student model on the labeled data
  • Helps on machine translation (100k parallel, 3.8M monolingual samples)
  • Beam search, when decoding the unlabeled data, contributes a bit to the gain (compared to sampling from the teacher’s output distribution)
  • Dropout, while training on the pseudo-data, accounts for most of the gain