SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Fig. 31 gives details on the neural network architectures used for the dynamics and measurement models within the PF for the Deepmind maze tracking task.
D.6 House3D Tracking Task
In this section we give additional task specific information and network architecture details for the House3D tracking task.
D.6.1 Additional Experiment Details
When training MDPF using the Negative Log-Likelihood (NLL) loss, we set the learning rate (LR) of the neural networks to be 0.0005 and the bandwidth learning rate to 0.00005. When training using MSE loss, we use 0.0001 and 0.00001 for the neural network and bandwidth learning rates respectively. For the discrete PF methods, we use  $\mathrm{LR} = 0.0005$  for training the neural network using the MSE loss. We use the same bandwidth for optimizing the bandwidth using the NLL loss when the neural networks are held fixed. For the LSTM, we use 0.001 as the learning rate for the LSTM layers and  $\mathrm{LR} = 0.0005$  when optimizing the bandwidth parameter using NLL.
We apply gradient norm clipping, clipping the norm of the gradients to be  $\leq 250.0$  when training with NLL loss. When training with MSE loss, we disable gradient norm clipping.
D.6.2 Network Architectures
Fig. 31 gives details on the neural network architectures used for the dynamics and measurement models within the PF for the House3D tracking task.
(a) Observation encoder.
(e) Dynamics model residual network. Output scaling scales the transnational dimensions to be within [-75, 75] and the unit vector representing angles to be within [-0.99, 0.99].
(b) Particle encoder for dynamics Model.
Figure 32: Neural architecture for the networks within the dynamics and measurement models for the House3D tracking task.
(f) Measurement model weights network.
D.7 LSTM Model Details
We train using LSTM [45] based recurrent neural network models for all tasks as a baseline comparison. For the LSTM, we use the same encoders as the PF methods. Fig. 33 shows the LSTM network architecture used for all tasks. We use the same output scaling the PF methods.
Figure 33: LSTM network architecture used for all tasks. The output is the 4D state which is then converted back into the 3D state using the inverse particle transform.