ACT lets RNNs dynamically adapt computation depth per input via a differentiable halting unit, yielding large gains on synthetic tasks and structural insights on language data.
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Generating Sequences With Recurrent Neural Networks
Canonical reference. 89% of citing Pith papers cite this work as background.
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abstract
This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
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A persistent homology loss enforces controllable connectivity in autoencoder latent spaces, improving one-class classification via kernel density estimation on the learned representations.
Scratchpad Patching decouples compute from patch size in byte-level language models by inserting entropy-triggered scratchpads to update patch context dynamically.
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Flamingo models reach new state-of-the-art few-shot results on image and video tasks by bridging frozen vision and language models with cross-attention layers trained on interleaved web-scale data.
T5 casts all NLP tasks as text-to-text generation, systematically explores pre-training choices, and reaches strong performance on summarization, QA, classification and other tasks via large-scale training on the Colossal Clean Crawled Corpus.
DiffMath is a symbol- and graph-aware latent diffusion framework that converts LaTeX structure into RelAST triplets and uses MathVAE plus MathDiT to generate structurally coherent handwritten expressions without explicit positional supervision.
Establishes that fading memory and solution stability hold generically in state-space systems for reservoir computing even without the echo state property, with a distributional attractor perspective for stochastic cases.
Compressive Transformer sets new records on WikiText-103 (17.1 ppl) and Enwik8 (0.97 bpc) via memory compression and introduces the PG-19 long-range language benchmark.
Separable convLSTMs cut parameters and FLOPs in video segmentation, delivering up to 15% faster GPU inference with similar or slightly lower accuracy.
Score-based generative modeling via multi-noise-level score matching and annealed Langevin dynamics produces samples on par with GANs and sets a new inception score record on CIFAR-10.
A framework unifies recent online RNN training algorithms along four axes and demonstrates performance clustering on synthetic tasks, indicating that gradient alignment is insufficient to explain success especially for stochastic methods.
RNN for arrivals paired with recurrent GAN for service times to model queuing dynamics without assuming specific inter-event distributions.
Anon optimizer uses tunable adaptivity and incremental delay update to achieve convergence guarantees and outperform existing methods on image classification, diffusion, and language modeling tasks.
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