Started full-time thesis around april/may 2023.
Track DST, Q3/4 start. Still "seminar course" ToDo. Has superapp/MusicDAO experience. Discussed as diverse as digital Euro and Web3 search engine (unsupervised learning, online learning, adversarial, byzantine, decentralised, personalised, local-first AI, edge-devices only, low-power hardware accelerated, and self-governance). Done machine Learning I class. (background: Samsung solution, ONE (On-device Neural Engine): A high-performance, on-device neural network inference framework.
Recommendation or semantic search? Alternative direction. Some overlap with the G-Rank follow-up project. Essential problem to solve: learning valid Creative Commons Bittorrent swarms.
class Seq2SeqEncoder(d2l.Encoder):
"""The RNN encoder for sequence to sequence learning."""
def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
dropout=0, **kwargs):
super(Seq2SeqEncoder, self).__init__(**kwargs)
# Embedding layer
self.embedding = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.GRU(embed_size, num_hiddens, num_layers,
dropout=dropout)
def forward(self, X, *args):
# The output `X` shape: (`batch_size`, `num_steps`, `embed_size`)
X = self.embedding(X)
# In RNN models, the first axis corresponds to time steps
X = X.permute(1, 0, 2)
# When state is not mentioned, it defaults to zeros
output, state = self.rnn(X)
# `output` shape: (`num_steps`, `batch_size`, `num_hiddens`)
# `state` shape: (`num_layers`, `batch_size`, `num_hiddens`)
return output, stateSecond sprint (strictly exploratory):
- get musicDAO running from source.
- read at least 4 master thesis works from the lab
Doing information retrieval msc course to prepare for this thesis
Literature survey initial idea: "nobody is doing autonomous AI" {unsupervised learning, online learning, adversarial, byzantine, decentralised, personalised, local-first AI, edge-devices only, low-power hardware accelerated, and self-governance}.
Started full-time thesis around april/may 2023.
Track DST, Q3/4 start. Still "seminar course" ToDo. Has superapp/MusicDAO experience. Discussed as diverse as digital Euro and Web3 search engine (unsupervised learning, online learning, adversarial, byzantine, decentralised, personalised, local-first AI, edge-devices only, low-power hardware accelerated, and self-governance). Done
machine Learning Iclass. (background: Samsung solution, ONE (On-device Neural Engine): A high-performance, on-device neural network inference framework.Recommendation or semantic search? Alternative direction. Some overlap with the G-Rank follow-up project. Essential problem to solve: learning valid Creative Commons Bittorrent swarms.
Second sprint (strictly exploratory):
Doing information retrieval msc course to prepare for this thesis
Literature survey initial idea: "nobody is doing autonomous AI" {unsupervised learning, online learning, adversarial, byzantine, decentralised, personalised, local-first AI, edge-devices only, low-power hardware accelerated, and self-governance}.