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Found 31 papers, 15 papers with code
Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less
This paper presents two simple improvements to the Self-Structuring AutoEncoder (Self-StrAE).
On the effect of curriculum learning with developmental data for grammar acquisition
Using BabyBERTa as a probe, we find that grammar acquisition is largely driven by exposure to speech data, and in particular through exposure to two of the BabyLM training corpora: AO-Childes and Open Subtitles.
DreamDecompiler: Bayesian Program Learning by Decompiling Amortised Knowledge
The cost of search is amortised by training a neural search policy, reducing search breadth and effectively "compiling" useful information to compose program solutions across tasks.
Autoencoding Conditional Neural Processes for Representation Learning
Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data.
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations.
Drawing out of Distribution with Neuro-Symbolic Generative Models
We then adopt a subset of the Omniglot challenge tasks, and evaluate its ability to generate new exemplars (both unconditionally and conditionally), and perform one-shot classification, showing that DooD matches the state of the art.
Adversarial Masking for Self-Supervised Learning
We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective.
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.
On Incorporating Inductive Biases into VAEs
InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es).
Learning Multimodal VAEs through Mutual Supervision
Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision.
Gradient Matching for Domain Generalization
We perform experiments on both the Wilds benchmark, which captures distribution shift in the real world, as well as datasets in DomainBed benchmark that focuses more on synthetic-to-real transfer.
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models
Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language.
Capturing Label Characteristics in VAEs
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels.
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
A Revised Generative Evaluation of Visual Dialogue
Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge.
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
In this work, we characterise successful learning of such models as the fulfillment of four criteria: i) implicit latent decomposition into shared and private subspaces, ii) coherent joint generation over all modalities, iii) coherent cross-generation across individual modalities, and iv) improved model learning for individual modalities through multi-modal integration.
Revisiting Reweighted Wake-Sleep
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.
Multitask Soft Option Learning
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.
Visual Dialogue without Vision or Dialogue
We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli.
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior.
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables.
DGPose: Deep Generative Models for Human Body Analysis
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
Structured Disentangled Representations
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
FlipDial: A Generative Model for Two-Way Visual Dialogue
We are the first to extend this paradigm to full two-way visual dialogue, where our model is capable of generating both questions and answers in sequence based on a visual input, for which we propose a set of novel evaluation measures and metrics.
Faithful Inversion of Generative Models for Effective Amortized Inference
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder.
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.
Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference.
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
When models are expressed as probabilistic programs, the models themselves are highly structured objects that can be used to derive annealing sequences that are more sensitive to domain structure.
Saying What You're Looking For: Linguistics Meets Video Search
We present an approach to searching large video corpora for video clips which depict a natural-language query in the form of a sentence.
Seeing What You're Told: Sentence-Guided Activity Recognition In Video
We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, thereby providing a medium, not only for top-down and bottom-up integration, but also for multi-modal integration between vision and language.
