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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arXiv preprint arXiv:1308.0850 (2013) 4, 5
16Pith papers citing it
16reference links
cs.CLtop field · 6 papers
UNVERDICTEDtop verdict bucket · 9 papers
why this work matters in Pith
Pith has found this work in 16 reviewed papers. Its strongest current cluster is cs.CL (6 papers). The largest review-status bucket among citing papers is UNVERDICTED (9 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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representative citing papers
Neural Turing Machines augment neural networks with differentiable external memory to learn algorithmic tasks such as copying, sorting, and associative recall from examples.
An attention-based encoder-decoder model achieves English-to-French translation performance comparable to phrase-based systems by automatically learning soft alignments.
A first-order stochastic optimizer that maintains bias-corrected exponential moving averages of the gradient and its square, dividing the former by the square root of the latter to set per-parameter step sizes.
Scratchpad Patching decouples compute from patch size in byte-level language models by inserting entropy-triggered scratchpads to update patch context dynamically.
The work introduces the ORVOS task, the ORVOSB benchmark with causal annotations across 210 videos, and a baseline using updated prompts plus a temporal token reservoir.
A single transformer model using a new markup representation generates functional floorplans from diverse conditions and outperforms prior task-specific methods on the RPLAN dataset.
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.
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.
CASHG explicitly models inter-character connectivity with a Character Context Encoder and bigram-aware Transformer decoder to produce style-consistent sentence trajectories, plus a new CSM evaluation metric.
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
Universal Transformers combine Transformer parallelism with recurrent updates and dynamic halting to achieve Turing-completeness under assumptions and outperform standard Transformers on algorithmic and language tasks.
Pith review generated a malformed one-line summary.
The paper surveys key large language models, their training methods, datasets, evaluation benchmarks, and future research directions in the field.
citing papers explorer
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Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models
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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CASHG: Context-Aware Stylized Online Handwriting Generation
CASHG explicitly models inter-character connectivity with a Character Context Encoder and bigram-aware Transformer decoder to produce style-consistent sentence trajectories, plus a new CSM evaluation metric.