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Learning to learn by acquiring knowledge across multiple tasks that transfers to new tasks
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Basierend auf der SOC-Berufsklassifikation
Building autonomous AI agents capable of reasoning, planning, and executing multi-step tasks
Learning from a small number of examples per class using metric learning and meta-learning
Techniques and frameworks for generating new data instances that match the distribution of training data
Advanced techniques for training and fine-tuning transformer-based language models at scale
Foundational understanding and practical implementation of transformer-based language models
Integrating and reasoning across multiple data modalities including text, images, audio, and video
| name | Meta-Learning |
| category | data-science |
| description | Learning to learn by acquiring knowledge across multiple tasks that transfers to new tasks |
I enable models to learn how to learn by extracting knowledge from multiple related tasks. Rather than optimizing for a single task, I learn representations and optimization strategies that allow rapid adaptation to new tasks with minimal data. This is fundamental for building systems that can generalize across diverse problems.
Task Distribution: The distribution of tasks from which meta-learning occurs.
Episode: A single meta-training iteration containing support and query sets.
Meta-Loss: The loss computed over query set performance after adaptation.
Task-Agnostic Parameters: Parameters that encode useful knowledge across tasks.
Adaptation Steps: The number of gradient steps taken before evaluating on query set.
Support Set: The small labeled set used for task-specific adaptation.
Query Set: The unlabeled or labeled set used to compute meta-loss.
Gradient-Based Meta-Learning: Methods that learn initializations amenable to quick adaptation.
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAML(nn.Module):
def __init__(self, model_fn, inner_lr=0.01, outer_lr=0.001):
super().__init__()
self.model = model_fn()
self.inner_lr = inner_lr
self.outer_lr = outer_lr
def forward(self, x):
return self.model(x)
def adaptation(self, support_x, support_y, first_order=True):
fast_weights = {name: param.clone() for name, param in self.model.named_parameters()}
logits = self._forward_with_weights(support_x, fast_weights)
loss = F.cross_entropy(logits, support_y)
grads = torch.autograd.grad(loss, fast_weights.values(), create_graph=not first_order)
fast_weights = {
name: param - self.inner_lr * grad
for (name, param), grad in zip(fast_weights.items(), grads)
}
return fast_weights
def meta_evaluation(self, support_x, support_y, query_x, query_y):
fast_weights = self.adaptation(support_x, support_y)
query_logits = self._forward_with_weights(query_x, fast_weights)
query_loss = F.cross_entropy(query_logits, query_y)
query_acc = (query_logits.argmax(dim=1) == query_y).float().mean()
return query_loss, query_acc
def meta_update(self, task_losses):
total_loss = sum(task_losses) / len(task_losses)
grads = torch.autograd.grad(total_loss, self.model.parameters())
with torch.no_grad():
for param, grad in zip(self.model.parameters(), grads):
param -= self.outer_lr * grad
return total_loss
def _forward_with_weights(self, x, weights):
x = F.conv2d(x, weights['conv1.weight'], weights.get('conv1.bias'))
x = F.relu(F.max_pool2d(x, 2))
x = F.conv2d(x, weights['conv2.weight'], weights.get('conv2.bias'))
x = F.relu(F.max_pool2d(x, 2))
x = x.view(x.size(0), -1)
x = F.linear(x, weights['fc1.weight'], weights['fc1.bias'])
return x
import torch
import torch.nn as nn
import torch.nn.functional as F
class MetaSGD(nn.Module):
def __init__(self, model_fn, inner_lr=0.01):
super().__init__()
self.model = model_fn()
self.inner_lr = nn.Parameter(torch.tensor([inner_lr] * len(list(self.model.parameters()))))
def adaptation(self, support_x, support_y):
fast_weights = []
for i, (name, param) in enumerate(self.model.named_parameters()):
grad = self._compute_grad(param, support_x, support_y)
new_param = param - self.inner_lr[i] * grad
fast_weights.append(new_param)
return fast_weights
def _compute_grad(self, param, support_x, support_y):
logits = self.model(support_x)
loss = F.cross_entropy(logits, support_y)
return torch.autograd.grad(loss, param)[0]
import torch
import torch.nn as nn
import torch.nn.functional as F
class PrototypicalNetworks(nn.Module):
def __init__(self, encoder_dim=64):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten()
)
def forward(self, support, query):
support_emb = self.encode(support)
query_emb = self.encode(query)
prototypes = self._compute_prototypes(support_emb)
logits = self._compute_logits(query_emb, prototypes)
return logits
def encode(self, x):
return self.encoder(x)
def _compute_prototypes(self, support_emb):
return support_emb.view(len(support_emb) // 5, 5, -1).mean(dim=1)
def _compute_logits(self, query_emb, prototypes):
dists = -torch.cdist(query_emb, prototypes, p=2)
return dists
import torch
import torch.nn as nn
import torch.nn.functional as F
class MemoryAugmentedNetwork(nn.Module):
def __init__(self, input_dim=784, memory_size=100, memory_dim=128):
super().__init__()
self.memory_size = memory_size
self.memory_dim = memory_dim
self.encoder = nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, memory_dim)
)
self.memory = nn.Parameter(torch.randn(memory_size, memory_dim))
self.key_head = nn.Linear(memory_dim, 1)
self.value_head = nn.Linear(memory_dim, memory_dim)
nn.init.xavier_uniform_(self.memory)
def forward(self, x, memory_read=True):
features = self.encoder(x)
if memory_read:
read_output = self._memory_read(features)
output = torch.cat([features, read_output], dim=1)
else:
output = features
return output
def _memory_read(self, query):
keys = self.key_head(self.memory)
values = self.value_head(self.memory)
similarities = query @ keys.t()
attention = F.softmax(similarities, dim=1)
read = attention @ values
return read
def write(self, new_memory, new_labels):
self.memory.data[new_labels] = new_memory
import torch
import torch.nn as nn
import torch.nn.functional as F
class MetaLearnerLSTM(nn.Module):
def __init__(self, input_dim=784, hidden_dim=100, output_dim=10):
super().__init__()
self.lstm = nn.LSTM(
input_size=output_dim + 1,
hidden_size=hidden_dim,
num_layers=1,
batch_first=True
)
self.encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU()
)
self.classifier = nn.Linear(hidden_dim, output_dim)
self.hidden_dim = hidden_dim
def forward(self, support_x, support_y, query_x):
batch_size = support_x.size(0)
n_support = support_x.size(1)
n_query = query_x.size(1)
support_features = self.encoder(support_x.view(-1, 784)).view(batch_size, n_support, -1)
support_input = torch.cat([
support_features,
F.one_hot(support_y, num_classes=10).float()
], dim=-1)
lstm_out, _ = self.lstm(support_input)
h_n = lstm_out[:, -1, :]
query_features = self.encoder(query_x.view(-1, 784)).view(batch_size, n_query, -1)
query_features = query_features + h_n.unsqueeze(1).expand_as(query_features)
logits = self.classifier(query_features)
return logits
Design task distributions that reflect the types of tasks expected at test time.
Use larger inner learning rates and smaller outer learning rates for MAML.
Apply first-order approximations to MAML for computational efficiency.
Balance the number of support and query examples per episode.
Use proper episode batching for stable meta-training.
Monitor both support and query accuracy during training.
Apply data augmentation to support sets to improve generalization.
Use appropriate evaluation protocols (multiple test episodes).
Consider task difficulty progression for curriculum meta-learning.
Validate on held-out task distributions to detect overfitting.