Run-length encodings (Corresp.)

By Solomon W. Golomb. 1966

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Citations in the corpus

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data (2019, methodology): “Therefore, we can optimally encode the distances using the Golomb code [34].”
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT (2020, unknown): “The implementation of GE [15] is the same as used in [14].”
Image and Video Compression With Neural Networks: A Review (2019, methodology): “For image compression, the early methods mainly realize compression by directly utilizing the entropy coding to reduce statistical redundancy within the image, such as Huffman coding [1], Golomb code [2] and arithmetic coding [3].”
Rethinking floating point for deep learning (2018, methodology): “It has no explicit size ﬁeld; the exponent is encoded using a Golomb-Rice preﬁx-free code [8, 22], with the exponent e encoded as a Golomb-Rice quotient and remainder ( q, r ) with q in unary and r in binary (in posit terminology, q is the regime ).”
Memory Capacities for Synaptic and Structural Plasticity (2010, background): “Optimal compression of the memory matrix A by Huffman (1952) or Golomb (1966) coding (the latter works in cases p1 → 0 or p1 → 1) can decrease the required physical memory by a factor according to the Shannon information I(p1) := −p1ldp1 − (1 − p1)ld(1 − p1) of a synaptic weight (see appendix A).”
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (2018, methodology): “Therefore, we can optimally encode the distances using the Golomb code [27].”