246 research outputs found
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
In Federated Learning, a global model is learned by aggregating model updates
computed at a set of independent client nodes, to reduce communication costs
multiple gradient steps are performed at each node prior to aggregation. A key
challenge in this setting is data heterogeneity across clients resulting in
differing local objectives which can lead clients to overly minimize their own
local objective, diverging from the global solution. We demonstrate that
individual client models experience a catastrophic forgetting with respect to
data from other clients and propose an efficient approach that modifies the
cross-entropy objective on a per-client basis by re-weighting the softmax
logits prior to computing the loss. This approach shields classes outside a
client's label set from abrupt representation change and we empirically
demonstrate it can alleviate client forgetting and provide consistent
improvements to standard federated learning algorithms. Our method is
particularly beneficial under the most challenging federated learning settings
where data heterogeneity is high and client participation in each round is low
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) methods can adapt large language
models to downstream tasks by training a small amount of newly added
parameters. In multi-task settings, PEFT adapters typically train on each task
independently, inhibiting transfer across tasks, or on the concatenation of all
tasks, which can lead to negative interference. To address this, Polytropon
(Ponti et al.) jointly learns an inventory of PEFT adapters and a routing
function to share variable-size sets of adapters across tasks. Subsequently,
adapters can be re-combined and fine-tuned on novel tasks even with limited
data. In this paper, we investigate to what extent the ability to control which
adapters are active for each task leads to sample-efficient generalization.
Thus, we propose less expressive variants where we perform weighted averaging
of the adapters before few-shot adaptation (Poly-mu) instead of learning a
routing function. Moreover, we introduce more expressive variants where
finer-grained task-adapter allocation is learned through a multi-head routing
function (Poly-S). We test these variants on three separate benchmarks for
multi-task learning. We find that Poly-S achieves gains on all three (up to 5.3
points on average) over strong baselines, while incurring a negligible
additional cost in parameter count. In particular, we find that instruction
tuning, where models are fully fine-tuned on natural language instructions for
each task, is inferior to modular methods such as Polytropon and our proposed
variants.Comment: Preprin
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